Feature Embedding Based Text Instance Grouping for Largely Spaced and Occluded Text Detection

Pan Gao, Qi Wan, Renwu Gao, Linlin Shen

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Auto-TLDR; Text Instance Embedding Based Feature Embeddings for Multiple Text Instance Grouping

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A text instance can be easily detected as multiple ones due to the large space between texts/characters, curved shape and partial occlusion. In this paper, a feature embedding based text instance grouping algorithm is proposed to solve this problem. To learn the feature space, a TIEM (Text Instance Embedding Module) is trained to minimize the within instance scatter and maximize the between instance scatter. Similarity between different text instances are measured in the feature space and merged if they meet certain conditions. Experimental results show that our approach can effectively connect text regions that belong to the same text instance. Competitive performance of our approach has been achieved on CTW1500, Total-Text, IC15 and a subset consists of texts selected from the three datasets, with large spacing and occlusions.

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An Accurate Threshold Insensitive Kernel Detector for Arbitrary Shaped Text

Xijun Qian, Yifan Liu, Yu-Bin Yang

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Auto-TLDR; TIKD: threshold insensitive kernel detector for arbitrary shaped text

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Recently, segmentation-based methods are popular in scene text detection due to the segmentation results can easily represent scene text of arbitrary shapes. However, previous works segment text instances the same as normal objects. It is obvious that the edge of the text instance differs from normal objects. In this paper, we propose a threshold insensitive kernel detector for arbitrary shaped text called TIKD, which includes a simple but stable base model and a new loss weight called Decay Loss Weight (DLW). By suppressing outlier pixels in a gradual way, the DLW can lead the network to detect more accurate text instances. Our method shows great power in accuracy and stability. It is worth mentioning that we achieve the precision, recall, f-measure of 88.7%, 83.7%, 86.1% respectively on the Total-Text dataset, with a fast speed of 16.3 frames per second. What’s more, even if we set the threshold in an extreme situation range from 0.1 to 0.9, our method can always achieve a stable f-measure over 79.9% on the Total-Text dataset.

TCATD: Text Contour Attention for Scene Text Detection

Ziling Hu, Wu Xingjiao, Jing Yang

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Auto-TLDR; Text Contour Attention Text Detector

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Segmentation-based approaches have enabled state-of-the-art performance in long or curved text detection tasks. However, false detection still is a challenge when two text instances are close to each other. To address this problem, in this paper, we propose a Text Contour Attention Text Detector (TCATD), which can locate scene text with arbitrary orientation and shape accurately. Different from previous work, TCATD focus on text contour map (TC), text center intensity map (TCI) and text kernel maps (TK). The TC can introduce text contour information, the TCI can help to learn the accurate text segmentation and the TK can generate the complete shape of text instances. Besides, we propose a Text Contour Attention Module to deal with contour information. After the Text Contour Attention Module, TC, TCI and TK will be obtained. Extensive experiments on ICDAR2015, CTW1500 and Total-Text demonstrate that the proposed method achieves the state-of-the-art performance.

Mutually Guided Dual-Task Network for Scene Text Detection

Mengbiao Zhao, Wei Feng, Fei Yin, Xu-Yao Zhang, Cheng-Lin Liu

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Auto-TLDR; A dual-task network for word-level and line-level text detection

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Scene text detection has been studied extensively. Existing methods detect either words or text lines and use either word-level or line-level annotated data for training. In this paper, we propose a dual-task network that can perform word-level and line-level text detection simultaneously and use training data of both levels of annotation to boost the performance. The dual-task network has two detection heads for word-level and line-level text detection, respectively. Then we propose a mutual guidance scheme for the joint training of the two tasks with two modules: line filtering module utilizes the output of the text line detector to filter out the non-text regions for the word detector, and word enhancing module provides prior positions of words for the text line detector depending on the output of the word detector. Experimental results of word-level and line-level text detection demonstrate the effectiveness of the proposed dual-task network and mutual guidance scheme, and the results of our method are competitive with state-of-the-art methods.

Scene Text Detection with Selected Anchors

Anna Zhu, Hang Du, Shengwu Xiong

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Auto-TLDR; AS-RPN: Anchor Selection-based Region Proposal Network for Scene Text Detection

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Object proposal technique with dense anchoring scheme for scene text detection were applied frequently to achieve high recall. It results in the significant improvement in accuracy but waste of computational searching, regression and classification. In this paper, we propose an anchor selection-based region proposal network (AS-RPN) using effective selected anchors instead of dense anchors to extract text proposals. The center, scales, aspect ratios and orientations of anchors are learnable instead of fixing, which leads to high recall and greatly reduced numbers of anchors. By replacing the anchor-based RPN in Faster RCNN, the AS-RPN-based Faster RCNN can achieve comparable performance with previous state-of-the-art text detecting approaches on standard benchmarks, including COCO-Text, ICDAR2013, ICDAR2015 and MSRA-TD500 when using single-scale and single model (ResNet50) testing only.

Transferable Adversarial Attacks for Deep Scene Text Detection

Shudeng Wu, Tao Dai, Guanghao Meng, Bin Chen, Jian Lu, Shutao Xia

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Auto-TLDR; Robustness of DNN-based STD methods against Adversarial Attacks

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Scene text detection (STD) aims to locate text in images and plays an important role in many computer vision tasks including automatic driving and text recognition systems. Recently, deep neural networks (DNNs) have been widely and successfully used in scene text detection, leading to plenty of DNN-based STD methods including regression-based and segmentation-based STD methods. However, recent studies have also shown that DNN is vulnerable to adversarial attacks, which can significantly degrade the performance of DNN models. In this paper, we investigate the robustness of DNN-based STD methods against adversarial attacks. To this end, we propose a generic and efficient attack method to generate adversarial examples, which are produced by adding small but imperceptible adversarial perturbation to the input images. Experiments on attacking four various models and a real-world STD engine of Google optical character recognition (OCR) show that the state-of-the-art DNN-based STD methods including regression-based and segmentation-based methods are vulnerable to adversarial attacks.

DUET: Detection Utilizing Enhancement for Text in Scanned or Captured Documents

Eun-Soo Jung, Hyeonggwan Son, Kyusam Oh, Yongkeun Yun, Soonhwan Kwon, Min Soo Kim

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Auto-TLDR; Text Detection for Document Images Using Synthetic and Real Data

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We present a novel approach to text detection for document images. For robust text detection of noisy scanned or captured document images, the advantages of multi-task learning are adopted by adding an auxiliary task of text enhancement. Consequently, our proposed model trains reducing noise and enhancing text regions as well as detecting text. To overcome the insufficiency of document image data for text detection, train data for our model are enriched with synthesized document images that are fully labeled for text detection and enhancement. For the effective use of synthetic and real data, the proposed model is trained in two phases. The first phase is training only synthetic data in a fully-supervised manner. Then real data with only detection labels are added in the second phase. The enhancement task for real data is weakly-supervised with information from detection labels. Our methods are demonstrated on a real document dataset with performances exceeding those of other methods. Also, we conducted ablations to analyze effects of the synthetic data, multi-task, and weak-supervision. Whereas the existing text detection studies mostly focus on the text in scenes, our proposed method is optimized to the applications for the text in scanned or captured documents.

Self-Training for Domain Adaptive Scene Text Detection

Yudi Chen, Wei Wang, Yu Zhou, Fei Yang, Dongbao Yang, Weiping Wang

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Auto-TLDR; A self-training framework for image-based scene text detection

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Though deep learning based scene text detection has achieved great progress, well-trained detectors suffer from severe performance degradation for different domains. In general, a tremendous amount of data is indispensable to train the detector in the target domain. However, data collection and annotation are expensive and time-consuming. To address this problem, we propose a self-training framework to automatically mine hard examples with pseudo-labels from unannotated videos or images. To reduce the noise of hard examples, a novel text mining module is implemented based on the fusion of detection and tracking results. Then, an image-to-video generation method is designed for the tasks that videos are unavailable and only images can be used. Experimental results on standard benchmarks, including ICDAR2015, MSRA-TD500, ICDAR2017 MLT, demonstrate the effectiveness of our self-training method. The simple Mask R-CNN adapted with self-training and fine-tuned on real data can achieve comparable or even superior results with the state-of-the-art methods.

Stratified Multi-Task Learning for Robust Spotting of Scene Texts

Kinjal Dasgupta, Sudip Das, Ujjwal Bhattacharya

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Auto-TLDR; Feature Representation Block for Multi-task Learning of Scene Text

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Gaining control over the dynamics of multi-task learning should help to unlock the potential of the deep network to a great extent. In the existing multi-task learning (MTL) approaches of deep network, all the parameters of its feature encoding part are subjected to adjustments corresponding to each of the underlying sub-tasks. On the other hand, different functional areas of human brain are responsible for distinct functions such as the Broca's area of the cerebrum is responsible for speech formation whereas its Wernicke's area is related to the language development etc. Inspired by this fact, in the present study, we propose to introduce a block (termed as Feature Representation Block) of connection weights spanned over a few successive layers of a deep multi-task learning architecture and stratify the same into distinct subsets for their adjustments exclusively corresponding to different sub-tasks. Additionally, we have introduced a novel regularization component for controlled training of this Feature Representation Block. The purpose of the development of this learning framework is efficient end-to-end recognition of scene texts. Simulation results of the proposed strategy on various benchmark scene text datasets such as ICDAR 2015, ICDAR 2017 MLT, COCO-Text and MSRA-TD500 have improved respective SOTA performance.

Recognizing Multiple Text Sequences from an Image by Pure End-To-End Learning

Zhenlong Xu, Shuigeng Zhou, Fan Bai, Cheng Zhanzhan, Yi Niu, Shiliang Pu

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Auto-TLDR; Pure End-to-End Learning for Multiple Text Sequences Recognition from Images

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We address a challenging problem: recognizing multiple text sequences from an image by pure end-to-end learning. It is twofold: 1) Multiple text sequences recognition. Each image may contain multiple text sequences of different content, location and orientation, we try to recognize all these texts in the image. 2) Pure end-to-end (PEE) learning.We solve the problem in a pure end-to-end learning way where each training image is labeled by only text transcripts of the contained sequences, without any geometric annotations. Most existing works recognize multiple text sequences from an image in a non-end-to-end (NEE) or quasi-end-to-end (QEE) way, in which each image is trained with both text transcripts and text locations. Only recently, a PEE method was proposed to recognize text sequences from an image where the text sequence was split to several lines in the image. However, it cannot be directly applied to recognizing multiple text sequences from an image. So in this paper, we propose a pure end-to-end learning method to recognize multiple text sequences from an image. Our method directly learns the probability distribution of multiple sequences conditioned on each input image, and outputs multiple text transcripts with a well-designed decoding strategy. To evaluate the proposed method, we construct several datasets mainly based on an existing public dataset and two real application scenarios. Experimental results show that the proposed method can effectively recognize multiple text sequences from images, and outperforms CTC-based and attention-based baseline methods.

Cascade Saliency Attention Network for Object Detection in Remote Sensing Images

Dayang Yu, Rong Zhang, Shan Qin

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Auto-TLDR; Cascade Saliency Attention Network for Object Detection in Remote Sensing Images

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Object detection in remote sensing images is a challenging task due to objects in the bird-view perspective appearing with arbitrary orientations. Though considerable progress has been made, there still exist challenges with the interference from complex backgrounds, dense arrangement, and large-scale variations. In this paper, we propose an oriented detector named Cascade Saliency Attention Network (CSAN), designed for comprehensively suppressing interference in remote sensing images. Specifically, we first combine context and pixel attention on feature maps to enhance saliency of objects for suppressing interference from backgrounds. Then, in cascade network, we apply instance segmentation on ROI to increase saliency of the central object, thus preventing object features from mutual interference in dense arrangement. Additionally, to alleviate large-scale variations, we devise a multi-scale merge module during FPN merging process to learn richer scale representations. Experimental results on DOTA and HRSC2016 datasets outperform other state-of-the-art object detection methods and verify the effectiveness of our method.

Automated Whiteboard Lecture Video Summarization by Content Region Detection and Representation

Bhargava Urala Kota, Alexander Stone, Kenny Davila, Srirangaraj Setlur, Venu Govindaraju

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Auto-TLDR; A Framework for Summarizing Whiteboard Lecture Videos Using Feature Representations of Handwritten Content Regions

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Lecture videos are rapidly becoming an invaluable source of information for students across the globe. Given the large number of online courses currently available, it is important to condense the information within these videos into a compact yet representative summary that can be used for search-based applications. We propose a framework to summarize whiteboard lecture videos by finding feature representations of detected handwritten content regions to determine unique content. We investigate multi-scale histogram of gradients and embeddings from deep metric learning for feature representation. We explicitly handle occluded, growing and disappearing handwritten content. Our method is capable of producing two kinds of lecture video summaries - the unique regions themselves or so-called key content and keyframes (which contain all unique content in a video segment). We use weighted spatio-temporal conflict minimization to segment the lecture and produce keyframes from detected regions and features. We evaluate both types of summaries and find that we obtain state-of-the-art peformance in terms of number of summary keyframes while our unique content recall and precision are comparable to state-of-the-art.

Local Gradient Difference Based Mass Features for Classification of 2D-3D Natural Scene Text Images

Lokesh Nandanwar, Shivakumara Palaiahnakote, Raghavendra Ramachandra, Tong Lu, Umapada Pal, Daniel Lopresti, Nor Badrul Anuar

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Auto-TLDR; Classification of 2D and 3D Natural Scene Images Using COLD

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Methods developed for normal 2D text detection do not work well for a text that is rendered using decorative, 3D effects. This paper proposes a new method for classification of 2D and 3D natural scene images such that an appropriate method can be chosen or modified according to the complexity of the individual classes. The proposed method explores local gradient differences for obtaining candidate pixels, which represent a stroke. To study the spatial distribution of candidate pixels, we propose a measure we call COLD, which is denser for pixels toward the center of strokes and scattered for non-stroke pixels. This observation leads us to introduce mass features for extracting the regular spatial pattern of COLD, which indicates a 2D text image. The extracted features are fed to a Neural Network (NN) for classification. The proposed method is tested on both a new dataset introduced in this work and a standard dataset assembled from different natural scene datasets, and compared to from existing methods to show its effectiveness. The approach improves text detection performance significantly after classification.

A Multi-Head Self-Relation Network for Scene Text Recognition

Zhou Junwei, Hongchao Gao, Jiao Dai, Dongqin Liu, Jizhong Han

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Auto-TLDR; Multi-head Self-relation Network for Scene Text Recognition

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The text embedded in scene images can be seen everywhere in our lives. However, recognizing text from natural scene images is still a challenge because of its diverse shapes and distorted patterns. Recently, advanced recognition networks generally treat scene text recognition as a sequence prediction task. Although achieving excellent performance, these recognition networks consider the feature map cells as independent individuals and update cells state without utilizing the information of their neighboring cells. And the local receptive field of traditional convolutional neural network (CNN) makes a single cell that cannot cover the whole text region in an image. Due to these issues, the existing recognition networks cannot extract the global context in a visual scene. To deal with the above problems, we propose a Multi-head Self-relation Network(MSRN) for scene text recognition in this paper. The MSRN consists of several multi-head self-relation layers, which is designed for extracting the global context of a visual scene, so that transforms a cell into a new cell that fuses the information of the related cells. Furthermore, experiments over several public datasets demonstrate that our proposed recognition network achieves superior performance on several benchmark datasets including IC03, IC13, IC15, SVT-Perspective.

RLST: A Reinforcement Learning Approach to Scene Text Detection Refinement

Xuan Peng, Zheng Huang, Kai Chen, Jie Guo, Weidong Qiu

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Auto-TLDR; Saccadic Eye Movements and Peripheral Vision for Scene Text Detection using Reinforcement Learning

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Within the research of scene text detection, some previous work has already achieved significant accuracy and efficiency. However, most of the work was generally done without considering about the implicit relationship between detection and eye movements. In this paper, we propose a new method for scene text detection especially for its refinement based on reinforcement learning. The idea of this method is inspired by Saccadic Eye Movements and Peripheral Vision. A saccade makes it possible for humans to orient the gaze to the location where a visual object has appeared. Peripheral vision gathers visual information of surroundings which provides supplement to foveal vision during gazing. We propose a simple pipeline, imitating the way human eyes do a saccade and collect peripheral information, to locate scene text roughly and to refine multi-scale vision field iteratively using reinforcement learning. For both training and evaluation, we use ICDAR2015 Challenge 4 dataset as a base and design several criteria to measure the feasibility of our work.

MEAN: A Multi-Element Attention Based Network for Scene Text Recognition

Ruijie Yan, Liangrui Peng, Shanyu Xiao, Gang Yao, Jaesik Min

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Auto-TLDR; Multi-element Attention Network for Scene Text Recognition

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Scene text recognition is a challenging problem due to the wide variances in content, style, orientation, and image quality of text instances in natural scene images. To learn the intrinsic representation of scene texts, a novel multi-element attention (MEA) mechanism is proposed to exploit geometric structures from local to global levels in the feature map extracted from a scene text image. The MEA mechanism is a generalized form of self-attention technique with the incorporation of graph structure modeling. The elements in feature maps are taken as the nodes of an undirected graph, and three kinds of adjacency matrices are introduced to aggregating information at local, neighborhood and global levels before calculating the attention weights. If only the local adjacency matrix is used, the MEA mechanism degenerates to a self-attention form. A multi-element attention network (MEAN) is implemented which includes a CNN for feature extraction, an encoder with MEA mechanism and a decoder for predicting text codes. Orientation positional encoding information is further added to the feature map output by the CNN, and a feature sequence as the encoder's input is obtained by element-level decomposition of the feature map. Experimental results show that MEAN has achieved state-of-the-art or competitive performance on public English scene text datasets. Further experiments and analyses conducted on both English and Chinese scene text datasets show that MEAN can handle horizontal, vertical, and irregular scene text samples.

SFPN: Semantic Feature Pyramid Network for Object Detection

Yi Gan, Wei Xu, Jianbo Su

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Auto-TLDR; SFPN: Semantic Feature Pyramid Network to Address Information Dilution Issue in FPN

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Feature Pyramid Network(FPN) employs a top-down path to enhance low level feature by utilizing high level feature.However, further improvement of detector is greatly hindered by the inner defect of FPN. The dilution issue in FPN is analyzed in this paper, and a new architecture named Semantic Feature Pyramid Network(SFPN) is introduced to address the information imbalance problem caused by information dilution. The proposed method consists of two simple and effective components: Semantic Pyramid Module(SPM) and Semantic Feature Fusion Module(SFFM). To compensate for the weaknesses of FPN, the semantic segmentation result is utilized as an extra information source in our architecture.By constructing a semantic pyramid based on the segmentation result and fusing it with FPN, feature maps at each level can obtain the necessary information without suffering from the dilution issue. The proposed architecture could be applied on many detectors, and non-negligible improvement could be achieved. Although this method is designed for object detection, other tasks such as instance segmentation can also largely benefit from it. The proposed method brings Faster R-CNN and Mask R-CNN with ResNet-50 as backbone both 1.8 AP improvements respectively. Furthermore, SFPN improves Cascade R-CNN with backbone ResNet-101 from 42.4 AP to 43.5 AP.

Robust Lexicon-Free Confidence Prediction for Text Recognition

Qi Song, Qianyi Jiang, Rui Zhang, Xiaolin Wei

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Auto-TLDR; Confidence Measurement for Optical Character Recognition using Single-Input Multi-Output Network

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Benefiting from the success of deep learning, Optical Character Recognition (OCR) is booming in recent years. As we all know, the text recognition results are vulnerable to slight perturbation in input images, thus a method for measuring how reliable the results are is crucial. In this paper, we present a novel method for confidence measurement given a text recognition result, which can be embedded in any text recognizer with little overheads. Our method consists of two stages with a coarse-to-fine style. The first stage generates multiple candidates for voting coarse scores by a Single-Input Multi-Output network (SIMO). The second stage calculates a refined confidence score referred by the voting result and the conditional probabilities of the Top-1 probable recognition sequence. Highly competitive performance is achieved on several standard benchmarks validates the efficiency and effectiveness of the proposed method. Moreover, it can be adopted in both Latin and non-Latin languages.

ReADS: A Rectified Attentional Double Supervised Network for Scene Text Recognition

Qi Song, Qianyi Jiang, Xiaolin Wei, Nan Li, Rui Zhang

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Auto-TLDR; ReADS: Rectified Attentional Double Supervised Network for General Scene Text Recognition

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In recent years, scene text recognition is always regarded as a sequence-to-sequence problem. Connectionist Temporal Classification (CTC) and Attentional sequence recognition (Attn) are two very prevailing approaches to tackle this problem while they may fail in some scenarios respectively. CTC concentrates more on every individual character but is weak in text semantic dependency modeling. Attn based methods have better context semantic modeling ability while tends to overfit on limited training data. In this paper, we elaborately design a Rectified Attentional Double Supervised Network (ReADS) for general scene text recognition. To overcome the weakness of CTC and Attn, both of them are applied in our method but with different modules in two supervised branches which can make a complementary to each other. Moreover, effective spatial and channel attention mechanisms are introduced to eliminate background noise and extract valid foreground information. Finally, a simple rectified network is implemented to rectify irregular text. The ReADS can be trained end-to-end and only word-level annotations are required. Extensive experiments on various benchmarks verify the effectiveness of ReADS which achieves state-of-the-art performance.

Vision-Based Layout Detection from Scientific Literature Using Recurrent Convolutional Neural Networks

Huichen Yang, William Hsu

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Auto-TLDR; Transfer Learning for Scientific Literature Layout Detection Using Convolutional Neural Networks

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We present an approach for adapting convolutional neural networks for object recognition and classification to scientific literature layout detection (SLLD), a shared subtask of several information extraction problems. Scientific publications contain multiple types of information sought by researchers in various disciplines, organized into an abstract, bibliography, and sections documenting related work, experimental methods, and results; however, there is no effective way to extract this information due to their diverse layout. In this paper, we present a novel approach to developing an end-to-end learning framework to segment and classify major regions of a scientific document. We consider scientific document layout analysis as an object detection task over digital images, without any additional text features that need to be added into the network during the training process. Our technical objective is to implement transfer learning via fine-tuning of pre-trained networks and thereby demonstrate that this deep learning architecture is suitable for tasks that lack very large document corpora for training. As part of the experimental test bed for empirical evaluation of this approach, we created a merged multi-corpus data set for scientific publication layout detection tasks. Our results show good improvement with fine-tuning of a pre-trained base network using this merged data set, compared to the baseline convolutional neural network architecture.

IBN-STR: A Robust Text Recognizer for Irregular Text in Natural Scenes

Xiaoqian Li, Jie Liu, Shuwu Zhang

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Auto-TLDR; IBN-STR: A Robust Text Recognition System Based on Data and Feature Representation

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Although text recognition methods based on deep neural networks have promising performance, there are still challenges due to the variety of text styles, perspective distortion, text with large curvature, and so on. To obtain a robust text recognizer, we have improved the performance from two aspects: data aspect and feature representation aspect. In terms of data, we transform the input images into S-shape distorted images in order to increase the diversity of training data. Besides, we explore the effects of different training data. In terms of feature representation, the combination of instance normalization and batch normalization improves the model's capacity and generalization ability. This paper proposes a robust text recognizer IBN-STR, which is an attention-based model. Through extensive experiments, the model analysis and comparison have been carried out from the aspects of data and feature representation, and the effectiveness of IBN-STR on both regular and irregular text instances has been verified. Furthermore, IBN-STR is an end-to-end recognition system that can achieve state-of-the-art performance.

Text Recognition - Real World Data and Where to Find Them

Klára Janoušková, Lluis Gomez, Dimosthenis Karatzas, Jiri Matas

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Auto-TLDR; Exploiting Weakly Annotated Images for Text Extraction

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We present a method for exploiting weakly annotated images to improve text extraction pipelines. The approach uses an arbitrary end-to-end text recognition system to obtain text region proposals and their, possibly erroneous, transcriptions. The proposed method includes matching of imprecise transcription to weak annotations and edit distance guided neighbourhood search. It produces nearly error-free, localised instances of scene text, which we treat as "pseudo ground truth" (PGT). We apply the method to two weakly-annotated datasets. Training with the extracted PGT consistently improves the accuracy of a state of the art recognition model, by 3.7 % on average, across different benchmark datasets (image domains) and 24.5 % on one of the weakly annotated datasets.

Weakly Supervised Attention Rectification for Scene Text Recognition

Chengyu Gu, Shilin Wang, Yiwei Zhu, Zheng Huang, Kai Chen

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Auto-TLDR; An auxiliary supervision branch for attention-based scene text recognition

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Scene text recognition has become a hot topic in recent years due to its booming real-life applications. Attention-based encoder-decoder framework has become one of the most popular frameworks especially in the irregular text scenario. However, the “attention drift” problem reduces the recognition performance for most existing attention-based scene text recognition methods. To solve this problem, we propose an auxiliary supervision branch along with the attention-based encoder-decoder framework. A new loss function is designed to refine the feature map and to help the attention region align the target character area. Compared with existing attention rectification mechanisms, our method does not require character-level annotations or introduce any additional trainable parameter. Furthermore, our method can improve the performance for both RNN-Attention and Scaled Dot-Product Attention. The experiment results on various benchmarks have demonstrated that the proposed approach outperforms the state-of-the-art methods in both regular and irregular text recognition scenarios.

CASNet: Common Attribute Support Network for Image Instance and Panoptic Segmentation

Xiaolong Liu, Yuqing Hou, Anbang Yao, Yurong Chen, Keqiang Li

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Auto-TLDR; Common Attribute Support Network for instance segmentation and panoptic segmentation

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Instance segmentation and panoptic segmentation is being paid more and more attention in recent years. In comparison with bounding box based object detection and semantic segmentation, instance segmentation can provide more analytical results at pixel level. Given the insight that pixels belonging to one instance have one or more common attributes of current instance, we bring up an one-stage instance segmentation network named Common Attribute Support Network (CASNet), which realizes instance segmentation by predicting and clustering common attributes. CASNet is designed in the manner of fully convolutional and can implement training and inference from end to end. And CASNet manages predicting the instance without overlaps and holes, which problem exists in most of current instance segmentation algorithms. Furthermore, it can be easily extended to panoptic segmentation through minor modifications with little computation overhead. CASNet builds a bridge between semantic and instance segmentation from finding pixel class ID to obtaining class and instance ID by operations on common attribute. Through experiment for instance and panoptic segmentation, CASNet gets mAP 32.8\% and PQ 59.0\% on Cityscapes validation dataset by joint training, and mAP 36.3\% and PQ 66.1\% by separated training mode. For panoptic segmentation, CASNet gets state-of-the-art performance on the Cityscapes validation dataset.

MagnifierNet: Learning Efficient Small-Scale Pedestrian Detector towards Multiple Dense Regions

Qi Cheng, Mingqin Chen, Yingjie Wu, Fei Chen, Shiping Lin

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Auto-TLDR; MagnifierNet: A Simple but Effective Small-Scale Pedestrian Detection Towards Multiple Dense Regions

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Despite the success of pedestrian detection, there is still a significant gap in the performance of the detection of pedestrians at different scales. Detecting small-scale pedestrians is extremely challenging due to the low resolution of their convolution features which is essential for downstream classifiers. To address this issue, we observed pedestrian datasets and found that pedestrians often gather together in crowded public places. Then we propose MagnifierNet, a simple but effective small-scale pedestrian detector towards multiple dense regions. MagnifierNet uses our proposed sweep-line based grouping algorithm to find dense regions based on the number of pedestrians in the grouped region. And we adopt a new definition of small-scale pedestrians through grid search and KL-divergence. Besides, our grouping method can also be used as a new strategy for pedestrian data augmentation. The ablation study demonstrates that MagnifierNet improves the representation of small-scale pedestrians. We validate the effectiveness of MagnifierNet on CityPersons and KITTI datasets. Experimental results show that MagnifierNet achieves the best small-scale pedestrian detection performance on CityPersons benchmark without any external data, and also achieves competitive performance for detecting small-scale pedestrians on KITTI dataset without bells and whistles.

Gaussian Constrained Attention Network for Scene Text Recognition

Zhi Qiao, Xugong Qin, Yu Zhou, Fei Yang, Weiping Wang

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Auto-TLDR; Gaussian Constrained Attention Network for Scene Text Recognition

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Scene text recognition has been a hot topic in computer vision. Recent methods adopt the attention mechanism for sequence prediction which achieve convincing results. However, we argue that the existing attention mechanism faces the problem of attention diffusion, in which the model may not focus on a certain character area. In this paper, we propose Gaussian Constrained Attention Network to deal with this problem. It is a 2D attention-based method integrated with a novel Gaussian Constrained Refinement Module, which predicts an additional Gaussian mask to refine the attention weights. Different from adopting an additional supervision on the attention weights simply, our proposed method introduce an explicit refinement. In this way, the attention weights will be more concentrated and the attention-based recognition network achieves better performance. The proposed Gaussian Constrained Refinement Module is flexible and can be applied to existing attention-based methods directly. The experiments on several benchmark datasets demonstrate the effectiveness of our proposed method. Our code has been available at https://github.com/Pay20Y/GCAN.

Sample-Aware Data Augmentor for Scene Text Recognition

Guanghao Meng, Tao Dai, Shudeng Wu, Bin Chen, Jian Lu, Yong Jiang, Shutao Xia

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Auto-TLDR; Sample-Aware Data Augmentation for Scene Text Recognition

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Deep neural networks (DNNs) have been widely used in scene text recognition, and achieved remarkable performance. Such DNN-based scene text recognizers usually require plenty of training data for training, but data collection and annotation is usually cost-expensive in practice. To alleviate this issue, data augmentation is often applied to train the scene text recognizers. However, existing data augmentation methods including affine transformation and elastic transformation methods suffer from the problems of under- and over-diversity, due to the complexity of text contents and shapes. In this paper, we propose a sample-aware data augmentor to transform samples adaptively based on the contents of samples. Specifically, our data augmentor consists of three parts: gated module, affine transformation module, and elastic transformation module. In our data augmentor, affine transformation module focuses on keeping the affinity of samples, while elastic transformation module aims to improve the diversity of samples. With the gated module, our data augmentor determines transformation type adaptively based on the properties of training samples and the recognizer capability during the training process. Besides, our framework introduces an adversarial learning strategy to optimize the augmentor and the recognizer jointly. Extensive experiments on scene text recognition benchmarks show that our sample-aware data augmentor significantly improves the performance of state-of-the-art scene text recognizer.

Triplet-Path Dilated Network for Detection and Segmentation of General Pathological Images

Jiaqi Luo, Zhicheng Zhao, Fei Su, Limei Guo

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Auto-TLDR; Triplet-path Network for One-Stage Object Detection and Segmentation in Pathological Images

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Deep learning has been widely applied in the field of medical image processing. However, compared with flourishing visual tasks in natural images, the progress achieved in pathological images is not remarkable, and detection and segmentation, which are among basic tasks of computer vision, are regarded as two independent tasks. In this paper, we make full use of existing datasets and construct a triplet-path network using dilated convolutions to cooperatively accomplish one-stage object detection and nuclei segmentation for general pathological images. First, in order to meet the requirement of detection and segmentation, a novel structure called triplet feature generation (TFG) is designed to extract high-resolution and multiscale features, where features from different layers can be properly integrated. Second, considering that pathological datasets are usually small, a location-aware and partially truncated loss function is proposed to improve the classification accuracy of datasets with few images and widely varying targets. We compare the performance of both object detection and instance segmentation with state-of-the-art methods. Experimental results demonstrate the effectiveness and efficiency of the proposed network on two datasets collected from multiple organs.

Construction Worker Hardhat-Wearing Detection Based on an Improved BiFPN

Chenyang Zhang, Zhiqiang Tian, Jingyi Song, Yaoyue Zheng, Bo Xu

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Auto-TLDR; A One-Stage Object Detection Method for Hardhat-Wearing in Construction Site

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Work in the construction site is considered to be one of the occupations with the highest safety risk factor. Therefore, safety plays an important role in construction site. One of the most fundamental safety rules in construction site is to wear a hardhat. To strengthen the safety of the construction site, most of the current methods use multi-stage method for hardhat-wearing detection. These methods have limitations in terms of adaptability and generalizability. In this paper, we propose a one-stage object detection method based on convolutional neural network. We present a multi-scale strategy that selects the high-resolution feature maps of DarkNet-53 to effectively identify small-scale hardhats. In addition, we propose an improved weighted bi-directional feature pyramid network (BiFPN), which could fuse more semantic features from more scales. The proposed method can not only detect hardhat-wearing, but also identify the color of the hardhat. Experimental results show that the proposed method achieves a mAP of 87.04%, which outperforms several state-of-the-art methods on a public dataset.

Dual Path Multi-Modal High-Order Features for Textual Content Based Visual Question Answering

Yanan Li, Yuetan Lin, Hongrui Zhao, Donghui Wang

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Auto-TLDR; TextVQA: An End-to-End Visual Question Answering Model for Text-Based VQA

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As a typical cross-modal problem, visual question answering (VQA) has received increasing attention from the communities of computer vision and natural language processing. Reading and reasoning about texts and visual contents in the images is a burgeoning and important research topic in VQA, especially for the visually impaired assistance applications. Given an image, it aims to predict an answer to a provided natural language question closely related to its textual contents. In this paper, we propose a novel end-to-end textual content based VQA model, which grounds question answering both on the visual and textual information. After encoding the image, question and recognized text words, it uses multi-modal factorized high-order modules and the attention mechanism to fuse question-image and question-text features respectively. The complex correlations among different features can be captured efficiently. To ensure the model's extendibility, it embeds candidate answers and recognized texts in a semantic embedding space and adopts semantic embedding based classifier to perform answer prediction. Extensive experiments on the newly proposed benchmark TextVQA demonstrate that the proposed model can achieve promising results.

Learning a Dynamic High-Resolution Network for Multi-Scale Pedestrian Detection

Mengyuan Ding, Shanshan Zhang, Jian Yang

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Auto-TLDR; Learningable Dynamic HRNet for Pedestrian Detection

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Pedestrian detection is a canonical instance of object detection in computer vision. In practice, scale variation is one of the key challenges, resulting in unbalanced performance across different scales. Recently, the High-Resolution Network (HRNet) has become popular because high-resolution feature representations are more friendly to small objects. However, when we apply HRNet for pedestrian detection, we observe that it improves for small pedestrians on one hand, but hurts the performance for larger ones on the other hand. To overcome this problem, we propose a learnable Dynamic HRNet (DHRNet) aiming to generate different network paths adaptive to different scales. Specifically, we construct a parallel multi-branch architecture and add a soft conditional gate module allowing for dynamic feature fusion. Both branches share all the same parameters except the soft gate module. Experimental results on CityPersons and Caltech benchmarks indicate that our proposed dynamic HRNet is more capable of dealing with pedestrians of various scales, and thus improves the performance across different scales consistently.

Bidirectional Matrix Feature Pyramid Network for Object Detection

Wei Xu, Yi Gan, Jianbo Su

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Auto-TLDR; BMFPN: Bidirectional Matrix Feature Pyramid Network for Object Detection

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Feature pyramids are widely used to improve scale invariance for object detection. Most methods just map the objects to feature maps with relevant square receptive fields, but rarely pay attention to the aspect ratio variation, which is also an important property of object instances. It will lead to a poor match between rectangular objects and assigned features with square receptive fields, thus preventing from accurate recognition and location. Besides, the information propagation among feature layers is sparse, namely, each feature in the pyramid may mainly or only contain single-level information, which is not representative enough for classification and localization sub-tasks. In this paper, Bidirectional Matrix Feature Pyramid Network (BMFPN) is proposed to address these issues. It consists of three modules: Diagonal Layer Generation Module (DLGM), Top-down Module (TDM) and Bottom-up Module (BUM). First, multi-level features extracted by backbone are fed into DLGM to produce the base features. Then these base features are utilized to construct the final feature pyramid through TDM and BUM in series. The receptive fields of the designed feature layers in BMFPN have various scales and aspect ratios. Objects can be correctly assigned to appropriate and representative feature maps with relevant receptive fields depending on its scale and aspect ratio properties. Moreover, TDM and BUM form bidirectional and reticular information flow, which effectively fuses multi level information in top-down and bottom-up manner respectively. To evaluate the effectiveness of our proposed architecture, an end-toend anchor-free detector is designed and trained by integrating BMFPN into FCOS. And the center ness branch in FCOS is modified with our Gaussian center-ness branch (GCB), which brings another slight improvement. Without bells and whistles, our method gains +3.3%, +2.4% and +2.6% AP on MS COCO dataset from baselines with ResNet-50, ResNet-101 and ResNeXt-101 backbones, respectively.

Text Recognition in Real Scenarios with a Few Labeled Samples

Jinghuang Lin, Cheng Zhanzhan, Fan Bai, Yi Niu, Shiliang Pu, Shuigeng Zhou

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Auto-TLDR; Few-shot Adversarial Sequence Domain Adaptation for Scene Text Recognition

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Scene text recognition (STR) is still a hot research topic in computer vision field due to its various applications. Existing works mainly focus on learning a general model with a huge number of synthetic text images to recognize unconstrained scene texts, and have achieved substantial progress. However, these methods are not quite applicable in many real-world scenarios where 1) high recognition accuracy is required, while 2) labeled samples are lacked. To tackle this challenging problem, this paper proposes a few-shot adversarial sequence domain adaptation (FASDA) approach to build sequence adaptation between the synthetic source domain (with many synthetic labeled samples) and a specific target domain (with only some or a few real labeled samples). This is done by simultaneously learning each character’s feature representation with an attention mech- anism and establishing the corresponding character-level latent subspace with adversarial learning. Our approach can maximize the character-level confusion between the source domain and the target domain, thus achieves the sequence-level adaptation with even a small number of labeled samples in the target domain. Extensive experiments on various datasets show that our method significantly outperforms the finetuning scheme, and obtains comparable performance to the state-of-the-art STR methods.

2D License Plate Recognition based on Automatic Perspective Rectification

Hui Xu, Zhao-Hong Guo, Da-Han Wang, Xiang-Dong Zhou, Yu Shi

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Auto-TLDR; Perspective Rectification Network for License Plate Recognition

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License plate recognition (LPR) remains a challenging task in face of some difficulties such as image deformation and multi-line character distribution. Text rectification that is crucial to eliminate the effects of image deformation has attracted increasing attentions in scene text recognition. However, current text rectification methods are not designed specifically for LPR, which did not take the features of plate deformation into account. Considering the fact that a license plate (LP) can only generate perspective distortion in the image due to its rigid feature, in this paper we propose a novel perspective rectification network (PRN) to automatically estimate the perspective transformation and rectify the distorted LP accordingly. For recognition, we propose a location-aware 2D attention based recognition network that is capable of recognizing both single-line and double-line plates with perspective deformation. The rectification network and recognition network are connected for end-to-end training. Experiments on common datasets show that the proposed method achieves the state-of-the-art performance, demonstrating the effectiveness of the proposed approach.

A Fast and Accurate Object Detector for Handwritten Digit String Recognition

Jun Guo, Wenjing Wei, Yifeng Ma, Cong Peng

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Auto-TLDR; ChipNet: An anchor-free object detector for handwritten digit string recognition

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Focusing on handwritten digit string recognition (HDSR), we propose an anchor-free object detector called ChipNet, where a novel encoding method is designed. The input image is divided into columns, and then these columns are encoded by the ground truth. The adjacent columns are responsible for detecting the same target so that it can well address the class-imbalanced problem meanwhile reducing the network computation. ChipNet is composed of convolutional and bidirectional long short term memory networks. Different from the typical detectors, it doesn't use region proposals, anchors or regions of interest pooling. Hence, it can overcome the shortages of anchor-based and dense detectors in HDSR. The experiments are implemented on the synthetic digit strings, the CVL HDS database, and the ORAND-CAR-A & B databases. The high accuracies, which surpass the reported results by a large margin (up to 6.62%), are achieved. Furthermore, it gets 219 FPS speed on 160*32 px resolution images when using a Tesla P100 GPU. The results also show that ChipNet can handle touching, connecting and arbitrary length digit strings, and the obtained accuracies in HDSR are as high as the ones in single handwritten digit recognition.

End-To-End Hierarchical Relation Extraction for Generic Form Understanding

Tuan Anh Nguyen Dang, Duc-Thanh Hoang, Quang Bach Tran, Chih-Wei Pan, Thanh-Dat Nguyen

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Auto-TLDR; Joint Entity Labeling and Link Prediction for Form Understanding in Noisy Scanned Documents

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Form understanding is a challenging problem which aims to recognize semantic entities from the input document and their hierarchical relations. Previous approaches face a significant difficulty dealing with the complexity of the task, thus treat these objectives separately. To this end, we present a novel deep neural network to jointly perform both Entity Labeling and link prediction in an end-to-end fashion. Our model extends the Multi-stage Attentional U-Net architecture with the Part-Intensity Fields and Part-Association Fields for link prediction, enriching the spatial information flow with the additional supervision from Entity Linking. We demonstrate the effectiveness of the model on the \textit{Form Understanding in Noisy Scanned Documents} \textit{(FUNSD)} dataset, where our method substantially outperforms the original model and state-of-the-art baselines in both Entity Labeling and Entity Linking task.

Watch Your Strokes: Improving Handwritten Text Recognition with Deformable Convolutions

Iulian Cojocaru, Silvia Cascianelli, Lorenzo Baraldi, Massimiliano Corsini, Rita Cucchiara

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Auto-TLDR; Deformable Convolutional Neural Networks for Handwritten Text Recognition

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Handwritten Text Recognition (HTR) in free-layout pages is a valuable yet challenging task which aims to automatically understand handwritten texts. State-of-the-art approaches in this field usually encode input images with Convolutional Neural Networks, whose kernels are typically defined on a fixed grid and focus on all input pixels independently. However, this is in contrast with the sparse nature of handwritten pages, in which only pixels representing the ink of the writing are useful for the recognition task. Furthermore, the standard convolution operator is not explicitly designed to take into account the great variability in shape, scale, and orientation of handwritten characters. To overcome these limitations, we investigate the use of deformable convolutions for handwriting recognition. This type of convolution deform the convolution kernel according to the content of the neighborhood, and can therefore be more adaptable to geometric variations and other deformations of the text. Experiments conducted on the IAM and RIMES datasets demonstrate that the use of deformable convolutions is a promising direction for the design of novel architectures for handwritten text recognition.

An Integrated Approach of Deep Learning and Symbolic Analysis for Digital PDF Table Extraction

Mengshi Zhang, Daniel Perelman, Vu Le, Sumit Gulwani

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Auto-TLDR; Deep Learning and Symbolic Reasoning for Unstructured PDF Table Extraction

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Deep learning has shown great success at interpreting unstructured data such as object recognition in images. Symbolic/logical-reasoning techniques have shown great success in interpreting structured data such as table extraction in webpages, custom text files, spreadsheets. The tables in PDF documents are often generated from such structured sources (text-based Word/Latex documents, spreadsheets, webpages) but end up being unstructured. We thus explore novel combinations of deep learning and symbolic reasoning techniques to build an effective solution for PDF table extraction. We evaluate effectiveness without granting partial credit for matching part of a table (which may cause silent errors in downstream data processing). Our method achieves a 0.725 F1 score (vs. 0.339 for the state-of-the-art) on detecting correct table bounds---a much stricter metric than the common one of detecting characters within tables---in a well known public benchmark (ICDAR 2013) and a 0.404 F1 score (vs. 0.144 for the state-of-the-art) on our private benchmark with more widely varied table structures.

Text Baseline Recognition Using a Recurrent Convolutional Neural Network

Matthias Wödlinger, Robert Sablatnig

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Auto-TLDR; Automatic Baseline Detection of Handwritten Text Using Recurrent Convolutional Neural Network

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The detection of baselines of text is a necessary pre-processing step for many modern methods of automatic handwriting recognition. In this work a two-stage system for the automatic detection of text baselines of handwritten text is presented. In a first step pixel-wise segmentation on the document image is performed to classify pixels as baselines, start points and end points. This segmentation is then used to extract the start points of lines. Starting from these points the baseline is extracted using a recurrent convolutional neural network that directly outputs the baseline coordinates. This method allows the direct extraction of baseline coordinates as the output of a neural network without the use of any post processing steps. The model is evaluated on the cBAD dataset from the ICDAR 2019 competition on baseline detection.

A Novel Region of Interest Extraction Layer for Instance Segmentation

Leonardo Rossi, Akbar Karimi, Andrea Prati

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Auto-TLDR; Generic RoI Extractor for Two-Stage Neural Network for Instance Segmentation

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Given the wide diffusion of deep neural network architectures for computer vision tasks, several new applications are nowadays more and more feasible. Among them, a particular attention has been recently given to instance segmentation, by exploiting the results achievable by two-stage networks (such as Mask R-CNN or Faster R-CNN), derived from R-CNN. In these complex architectures, a crucial role is played by the Region of Interest (RoI) extraction layer, devoted to extract a coherent subset of features from a single Feature Pyramid Network (FPN) layer attached on top of a backbone. This paper is motivated by the need to overcome to the limitations of existing RoI extractors which select only one (the best) layer from FPN. Our intuition is that all the layers of FPN retain useful information. Therefore, the proposed layer (called Generic RoI Extractor - GRoIE) introduces non-local building blocks and attention mechanisms to boost the performance. A comprehensive ablation study at component level is conducted to find the best set of algorithms and parameters for the GRoIE layer. Moreover, GRoIE can be integrated seamlessly with every two-stage architecture for both object detection and instance segmentation tasks. Therefore, the improvements brought by the use of GRoIE in different state-of-the-art architectures are also evaluated. The proposed layer leads up to gain a 1.1% AP on bounding box detection and 1.7% AP on instance segmentation. The code is publicly available on GitHub repository at https://github.com/IMPLabUniPr/mmdetection-groie

Object Detection Model Based on Scene-Level Region Proposal Self-Attention

Yu Quan, Zhixin Li, Canlong Zhang, Huifang Ma

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Auto-TLDR; Exploiting Semantic Informations for Object Detection

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The improvement of object detection performance is mostly focused on the extraction of local information near the region of interest in the image, which results in detection performance in this area being unable to achieve the desired effect. First, a depth-wise separable convolution network(D_SCNet-127 R-CNN) is built on the backbone network. Considering the importance of scene and semantic informations for visual recognition, the feature map is sent into the branch of the semantic segmentation module, region proposal network module, and the region proposal self-attention module to build the network of scene-level and region proposal self-attention module. Second, a deep reinforcement learning was utilized to achieve accurate positioning of border regression, and the calculation speed of the whole model was improved through implementing a light-weight head network. This model can effectively solve the limitation of feature extraction in traditional object detection and obtain more comprehensive detailed features. The experimental verification on MSCOCO17, VOC12, and Cityscapes datasets shows that the proposed method has good validity and scalability.

Forground-Guided Vehicle Perception Framework

Kun Tian, Tong Zhou, Shiming Xiang, Chunhong Pan

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Auto-TLDR; A foreground segmentation branch for vehicle detection

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As the basis of advanced visual tasks such as vehicle tracking and traffic flow analysis, vehicle detection needs to accurately predict the position and category of vehicle objects. In the past decade, deep learning based methods have made great progress. However, we also notice that some existing cases are not studied thoroughly. First, false positive on the background regions is one of the critical problems. Second, most of the previous approaches only optimize a single vehicle detection model, ignoring the relationship between different visual perception tasks. In response to the above two findings, we introduce a foreground segmentation branch for the first time, which can predict the pixel level of vehicles in advance. Furthermore, two attention modules are designed to guide the work of the detection branch. The proposed method can be easily grafted into the one-stage and two-stage detection framework. We evaluate the effectiveness of our model on LSVH, a dataset with large variations in vehicle scales, and achieve the state-of-the-art detection accuracy.

Image-Based Table Cell Detection: A New Dataset and an Improved Detection Method

Dafeng Wei, Hongtao Lu, Yi Zhou, Kai Chen

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Auto-TLDR; TableCell: A Semi-supervised Dataset for Table-wise Detection and Recognition

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The topic of table detection and recognition has been spotlighted in recent years, however, the latest works only aim at the coarse scene in table-wise detection. In this paper, we present TableCell, a new image-based dataset which contains 5262 samples with 170K high precision cell-wised annotations based on a novel semi-supervised method.. Several classical deep learning detection models are evaluated to build a strong baseline using the proposed dataset. Furthermore, we come up with an efficient table projection method to facilitate capturing long-range global feature, which consists of row projection and column projection. Experiments demonstrate that our proposed method improves the accuracy of table detection. Our dataset and code will be made available at https://github.com/weidafeng/TableCell upon publication.

P2 Net: Augmented Parallel-Pyramid Net for Attention Guided Pose Estimation

Luanxuan Hou, Jie Cao, Yuan Zhao, Haifeng Shen, Jian Tang, Ran He

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Auto-TLDR; Parallel-Pyramid Net with Partial Attention for Human Pose Estimation

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The target of human pose estimation is to determine the body parts and joint locations of persons in the image. Angular changes, motion blur and occlusion etc. in the natural scenes make this task challenging, while some joints are more difficult to be detected than others. In this paper, we propose an augmented Parallel-Pyramid Net (P^2Net) with an partial attention module. During data preprocessing, we proposed a differentiable auto data augmentation (DA^2) method in which sequences of data augmentations are formulated as a trainable and operational Convolution Neural Network (CNN) component. DA^2 improves the training efficiency and effectiveness. A parallel pyramid structure is followed to compensate the information loss introduced by the network. For the information loss problem in the backbone network, we optimize the backbone network by adopting a new parallel structure without increasing the overall computational complexity. To further refine the predictions after completion of global predictions, an Partial Attention Module (PAM) is defined to extract weighted features from different scale feature maps generated by the parallel pyramid structure. Compared with the traditional up-sampling refining, PAM can better capture the relationship between channels. Experiments corroborate the effectiveness of our proposed method. Notably, our method achieves the best performance on the challenging MSCOCO and MPII datasets.

Boosting High-Level Vision with Joint Compression Artifacts Reduction and Super-Resolution

Xiaoyu Xiang, Qian Lin, Jan Allebach

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Auto-TLDR; A Context-Aware Joint CAR and SR Neural Network for High-Resolution Text Recognition and Face Detection

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Due to the limits of bandwidth and storage space, digital images are usually down-scaled and compressed when transmitted over networks, resulting in loss of details and jarring artifacts that can lower the performance of high-level visual tasks. In this paper, we aim to generate an artifact-free high-resolution image from a low-resolution one compressed with an arbitrary quality factor by exploring joint compression artifacts reduction (CAR) and super-resolution (SR) tasks. First, we propose a context-aware joint CAR and SR neural network (CAJNN) that integrates both local and non-local features to solve CAR and SR in one-stage. Finally, a deep reconstruction network is adopted to predict high quality and high-resolution images. Evaluation on CAR and SR benchmark datasets shows that our CAJNN model outperforms previous methods and also takes 26.2% less runtime. Based on this model, we explore addressing two critical challenges in high-level computer vision: optical character recognition of low-resolution texts, and extremely tiny face detection. We demonstrate that CAJNN can serve as an effective image preprocessing method and improve the accuracy for real-scene text recognition (from 85.30% to 85.75%) and the average precision for tiny face detection (from 0.317 to 0.611).

Cost-Effective Adversarial Attacks against Scene Text Recognition

Mingkun Yang, Haitian Zheng, Xiang Bai, Jiebo Luo

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Auto-TLDR; Adversarial Attacks on Scene Text Recognition

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Scene text recognition is a challenging task due to the diversity in text appearance and complexity of natural scenes. Thanks to the development of deep learning and the large volume of training data, scene text recognition has made impressive progress in recent years. However, recent research on adversarial examples has shown that deep learning models are vulnerable to adversarial input with imperceptible changes. As one of the most practical tasks in computer vision, scene text recognition is also facing huge security risks. To our best knowledge, there has been no work on adversarial attacks against scene text recognition. To investigate its effects on scene text recognition, we make the first attempt to attack the state-of-the-art scene text recognizer, i.e., attention-based recognizer. To that end, we first adjust the objective function designed for non-sequential tasks, such as image classification, semantic segmentation and image retrieval, to the sequential form. We then propose a novel and effective objective function to further reduce the amount of perturbation while achieving a higher attack success rate. Comprehensive experiments on several standard benchmarks clearly demonstrate effective adversarial effects on scene text recognition by the proposed attacks.

Multiple Document Datasets Pre-Training Improves Text Line Detection with Deep Neural Networks

Mélodie Boillet, Christopher Kermorvant, Thierry Paquet

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Auto-TLDR; A fully convolutional network for document layout analysis

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In this paper, we introduce a fully convolutional network for the document layout analysis task. While state-of-the-art methods are using models pre-trained on natural scene images, our method relies on a U-shaped model trained from scratch for detecting objects from historical documents. We consider the line segmentation task and more generally the layout analysis problem as a pixel-wise classification task then our model outputs a pixel-labeling of the input images. We show that our method outperforms state-of-the-art methods on various datasets and also demonstrate that the pre-trained parts on natural scene images are not required to reach good results. In addition, we show that pre-training on multiple document datasets can improve the performances. We evaluate the models using various metrics to have a fair and complete comparison between the methods.

Hybrid Cascade Point Search Network for High Precision Bar Chart Component Detection

Junyu Luo, Jinpeng Wang, Chin-Yew Lin

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Auto-TLDR; Object Detection of Chart Components in Chart Images Using Point-based and Region-Based Object Detection Framework

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Charts are commonly used for data visualization. One common form of chart distribution is in its image form. To enable machine comprehension of chart images, precise detection of chart components in chart images is a critical step. Existing image object detection methods do not perform well in chart component detection which requires high boundary detection precision. And traditional rule-based approaches lack enough generalization ability. In order to address this problem, we design a novel two-stage object detection framework that combines point-based and region-based ideas, by simulating the process that human creating bounding boxes for objects. The experiment on our labeled ChartDet dataset shows our method greatly improves the performance of chart object detection. We further extend our method to a general object detection task and get comparable performance.

Detecting Objects with High Object Region Percentage

Fen Fang, Qianli Xu, Liyuan Li, Ying Gu, Joo-Hwee Lim

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Auto-TLDR; Faster R-CNN for High-ORP Object Detection

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Object shape is a subtle but important factor for object detection. It has been observed that the object-region-percentage (ORP) can be utilized to improve detection accuracy for elongated objects, which have much lower ORPs than other types of objects. In this paper, we propose an approach to improve the detection performance for objects whose ORPs are relatively higher.To address the problem of high-ORP object detection, we propose a method consisting of three steps. First, we adjust the ground truth bounding boxes of high-ORP objects to an optimal range. Second, we train an object detector, Faster R-CNN, based on adjusted bounding boxes to achieve high recall. Finally, we train a DCNN to learn the adjustment ratios towards four directions and adjust detected bounding boxes of objects to get better localization for higher precision. We evaluate the effectiveness of our method on 12 high-ORP objects in COCO and 8 objects in a proprietary gearbox dataset. The experimental results show that our method can achieve state-of-the-art performance on these objects while costing less resources in training and inference stages.