A Few-Shot Learning Approach for Historical Ciphered Manuscript Recognition

Mohamed Ali Souibgui, Alicia Fornés, Yousri Kessentini, Crina Tudor

Responsive image

Auto-TLDR; Handwritten Ciphers Recognition Using Few-Shot Object Detection

Slides

Encoded (or ciphered) manuscripts are a special type of historical documents that contain encrypted text. The automatic recognition of this kind of documents is challenging because: 1) the cipher alphabet changes from one document to another, 2) there is a lack of annotated corpus for training and 3) touching symbols make the symbol segmentation difficult and complex. To overcome these difficulties, we propose a novel method for handwritten ciphers recognition based on few-shot object detection. Our method first detects all symbols of a given alphabet in a line image, and then a decoding step maps the symbol similarity scores to the final sequence of transcribed symbols. By training on synthetic data, we show that the proposed architecture is able to recognize handwritten ciphers with unseen alphabets. In addition, if few labeled pages with the same alphabet are used for fine tuning, our method surpasses existing unsupervised and supervised HTR methods for ciphers recognition.

Similar papers

The HisClima Database: Historical Weather Logs for Automatic Transcription and Information Extraction

Verónica Romero, Joan Andreu Sánchez

Responsive image

Auto-TLDR; Automatic Handwritten Text Recognition and Information Extraction from Historical Weather Logs

Slides Poster Similar

Knowing the weather and atmospheric conditions from the past can help weather researchers to generate models like the ones used to predict how weather conditions are likely to change as global temperatures continue to rise. Many historical weather records are available from the past registered on a systemic basis. Historical weather logs were registered in ships, when they were on the high seas, recording daily weather conditions such as: wind speed, temperature, coordinates, etc. These historical documents represent an important source of knowledge with valuable information to extract climatic information of several centuries ago. Such information is usually collected by experts that devote a lot of time. This paper presents a new database, compiled from a ship log mainly composed by handwritten tables that contain mainly numerical information, to support research in automatic handwriting recognition and information extraction. In addition, a study is presented about the capability of state-of-the-art handwritten text recognition systems and information extraction techniques, when applied to the presented database. Baseline results are reported for reference in future studies.

Watch Your Strokes: Improving Handwritten Text Recognition with Deformable Convolutions

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

Responsive image

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

Slides Poster Similar

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.

Recursive Recognition of Offline Handwritten Mathematical Expressions

Marco Cotogni, Claudio Cusano, Antonino Nocera

Responsive image

Auto-TLDR; Online Handwritten Mathematical Expression Recognition with Recurrent Neural Network

Slides Poster Similar

In this paper we propose a method for Offline Handwritten Mathematical Expression recognition. The method is a fast and accurate thanks to its architecture, which include both a Convolutional Neural Network and a Recurrent Neural Network. The CNN extracts features from the image to recognize and its output is provided to the RNN which produces the mathematical expression encoded in the LaTeX language. To process both sequential and non-sequential mathematical expressions we also included a deconvolutional module which, in a recursive way, segments the image for additional analysis trough a recursive process. The results obtained show a very high accuracy obtained on a large handwritten data set of 9100 samples of handwritten expressions.

Writer Identification Using Deep Neural Networks: Impact of Patch Size and Number of Patches

Akshay Punjabi, José Ramón Prieto Fontcuberta, Enrique Vidal

Responsive image

Auto-TLDR; Writer Recognition Using Deep Neural Networks for Handwritten Text Images

Slides Poster Similar

Traditional approaches for the recognition or identification of the writer of a handwritten text image used to relay on heuristic knowledge about the shape and other features of the strokes of previously segmented characters. However, recent works have done significantly advances on the state of the art thanks to the use of various types of deep neural networks. In most of all of these works, text images are decomposed into patches, which are processed by the networks without any previous character or word segmentation. In this paper, we study how the way images are decomposed into patches impact recognition accuracy, using three publicly available datasets. The study also includes a simpler architecture where no patches are used at all - a single deep neural network inputs a whole text image and directly provides a writer recognition hypothesis. Results show that bigger patches generally lead to improved accuracy, achieving in one of the datasets a significant improvement over the best results reported so far.

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

Huichen Yang, William Hsu

Responsive image

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

Slides Poster Similar

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.

Improving Word Recognition Using Multiple Hypotheses and Deep Embeddings

Siddhant Bansal, Praveen Krishnan, C. V. Jawahar

Responsive image

Auto-TLDR; EmbedNet: fuse recognition-based and recognition-free approaches for word recognition using learning-based methods

Slides Poster Similar

We propose to fuse recognition-based and recognition-free approaches for word recognition using learning-based methods. For this purpose, results obtained using a text recognizer and deep embeddings (generated using an End2End network) are fused. To further improve the embeddings, we propose EmbedNet, it uses triplet loss for training and learns an embedding space where the embedding of the word image lies closer to its corresponding text transcription’s embedding. This updated embedding space helps in choosing the correct prediction with higher confidence. To further improve the accuracy, we propose a plug-and-play module called Confidence based Accuracy Booster (CAB). It takes in the confidence scores obtained from the text recognizer and Euclidean distances between the embeddings and generates an updated distance vector. This vector has lower distance values for the correct words and higher distance values for the incorrect words. We rigorously evaluate our proposed method systematically on a collection of books that are in the Hindi language. Our method achieves an absolute improvement of around 10% in terms of word recognition accuracy.

The DeepScoresV2 Dataset and Benchmark for Music Object Detection

Lukas Tuggener, Yvan Putra Satyawan, Alexander Pacha, Jürgen Schmidhuber, Thilo Stadelmann

Responsive image

Auto-TLDR; DeepScoresV2: an extended version of the DeepScores dataset for optical music recognition

Slides Poster Similar

In this paper, we present DeepScoresV2, an extended version of the DeepScores dataset for optical music recognition (OMR). We improve upon the original DeepScores dataset by providing much more detailed annotations, namely (a) annotations for 135 classes including fundamental symbols of non-fixed size and shape, increasing the number of annotated symbols by 23%; (b) oriented bounding boxes; (c) higher-level rhythm and pitch information (onset beat for all symbols and line position for noteheads); and (d) a compatibility mode for easy use in conjunction with the MUSCIMA++ dataset for OMR on handwritten documents. These additions open up the potential for future advancement in OMR research. Additionally, we release two state-of-the-art baselines for DeepScoresV2 based on Faster R-CNN and the Deep Watershed Detector. An analysis of the baselines shows that regular orthogonal bounding boxes are unsuitable for objects which are long, small, and potentially rotated, such as ties and beams, which demonstrates the need for detection algorithms that naturally incorporate object angles. Dataset, code and pre-trained models, as well as user instructions, are publicly available at https://tuggeluk.github.io/dsv2_preview/

Textual-Content Based Classification of Bundles of Untranscribed of Manuscript Images

José Ramón Prieto Fontcuberta, Enrique Vidal, Vicente Bosch, Carlos Alonso, Carmen Orcero, Lourdes Márquez

Responsive image

Auto-TLDR; Probabilistic Indexing for Text-based Classification of Manuscripts

Slides Poster Similar

Content-based classification of manuscripts is an important task that is generally performed in archives and libraries by experts with a wealth of knowledge on the manuscripts contents. Unfortunately, many manuscript collections are so vast that it is not feasible to rely solely on experts to perform this task. Current approaches for textual-content-based manuscript classification generally require the handwritten images to be first transcribed into text -- but achieving sufficiently accurate transcripts is generally unfeasible for large sets of historical manuscripts. We propose a new approach to automatically perform this classification task which does not rely on any explicit image transcripts. It is based on ``probabilistic indexing'', a relatively novel technology which allows to effectively represent the intrinsic word-level uncertainty generally exhibited by handwritten text images. We assess the performance of this approach on a large collection of complex manuscripts from the Spanish Archivo General de Indias, with promising results.

Recognizing Bengali Word Images - A Zero-Shot Learning Perspective

Sukalpa Chanda, Daniël Arjen Willem Haitink, Prashant Kumar Prasad, Jochem Baas, Umapada Pal, Lambert Schomaker

Responsive image

Auto-TLDR; Zero-Shot Learning for Word Recognition in Bengali Script

Slides Poster Similar

Zero-Shot Learning(ZSL) techniques could classify a completely unseen class, which it has never seen before during training. Thus, making it more apt for any real-life classification problem, where it is not possible to train a system with annotated data for all possible class types. This work investigates recognition of word images written in Bengali Script in a ZSL framework. The proposed approach performs Zero-Shot word recognition by coupling deep learned features procured from VGG16 architecture along with 13 basic shapes/stroke primitives commonly observed in Bengali script characters. As per the notion of ZSL framework those 13 basic shapes are termed as “Signature Attributes”. The obtained results are promising while evaluation was carried out in a Five-Fold cross-validation setup dealing with samples from 250 word classes.

Online Trajectory Recovery from Offline Handwritten Japanese Kanji Characters of Multiple Strokes

Hung Tuan Nguyen, Tsubasa Nakamura, Cuong Tuan Nguyen, Masaki Nakagawa

Responsive image

Auto-TLDR; Recovering Dynamic Online Trajectories from Offline Japanese Kanji Character Images for Handwritten Character Recognition

Slides Poster Similar

We propose a deep neural network-based method to recover dynamic online trajectories from offline handwritten Japanese kanji character images. It is a challenging task since Japanese kanji characters consist of multiple strokes. Our proposed model has three main components: Convolutional Neural Network-based encoder, Long Short-Term Memory Network-based decoder with an attention layer, and Gaussian Mixture Model (GMM). The encoder focuses on feature extraction while the decoder refers to the extracted features and generates time-sequences of GMM parameters. The attention layer is the key component for trajectory recovery. The GMM provides robustness to style variations so that the proposed model does not overfit to training samples. In the experiments, the proposed method is evaluated by both visual verification and handwritten character recognition. This is the first attempt to use online recovered trajectories to help improve the performance of offline handwriting recognition. Although the visual verification reveals some problems, the recognition experiments demonstrate the effect of trajectory recovery in improving the accuracy of offline handwritten character recognition when online recognition of the recovered trajectories are combined.

Text Baseline Recognition Using a Recurrent Convolutional Neural Network

Matthias Wödlinger, Robert Sablatnig

Responsive image

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

Slides Poster Similar

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 Fast and Accurate Object Detector for Handwritten Digit String Recognition

Jun Guo, Wenjing Wei, Yifeng Ma, Cong Peng

Responsive image

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

Slides Poster Similar

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.

Text Recognition - Real World Data and Where to Find Them

Klára Janoušková, Lluis Gomez, Dimosthenis Karatzas, Jiri Matas

Responsive image

Auto-TLDR; Exploiting Weakly Annotated Images for Text Extraction

Slides Poster Similar

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.

Learning to Sort Handwritten Text Lines in Reading Order through Estimated Binary Order Relations

Lorenzo Quirós, Enrique Vidal

Responsive image

Auto-TLDR; Automatic Reading Order of Text Lines in Handwritten Text Documents

Slides Similar

Recent advances in Handwritten Text Recognition and Document Layout Analysis make it possible to extract information from digitized documents and make them accessible beyond the archive shelves. But the reading order of the elements in those documents still is an open problem that has to be solved in order to provide that information with the correct structure. Most of the studies on the reading order task are rule-base approaches that focus on printed documents, while less attention has been paid to handwritten text documents. In this work we propose a new approach to automatically determine the reading order of text lines in handwritten text documents. The task is approached as a sorting problem where the order-relation operator is learned directly from examples. We demonstrate the effectiveness of our method on three different datasets.

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

Mélodie Boillet, Christopher Kermorvant, Thierry Paquet

Responsive image

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

Slides Similar

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.

LODENet: A Holistic Approach to Offline Handwritten Chinese and Japanese Text Line Recognition

Huu Tin Hoang, Chun-Jen Peng, Hung Tran, Hung Le, Huy Hoang Nguyen

Responsive image

Auto-TLDR; Logographic DEComposition Encoding for Chinese and Japanese Text Line Recognition

Slides Poster Similar

One of the biggest obstacles in Chinese and Japanese text line recognition is how to present their enormous character sets. The most common solution is to merely choose and represent a small subset of characters using one-hot encoding. However, such an approach is costly to describe huge character sets, and ignores their semantic relationships. Recent studies have attempted to utilize different encoding methods, but they struggle to build a bijection mapping. In this work, we propose a novel encoding method, called LOgographic DEComposition encoding (LODEC), that can efficiently perform a 1-to-1 mapping for all Chinese and Japanese characters with a strong awareness of semantic relationships. As such, LODEC enables to encode over 21,000 Chinese and Japanese characters by only 520 fundamental elements. Moreover, to handle the vast variety of handwritten texts in the two languages, we propose a novel deep learning (DL) architecture, called LODENet, together with an end-to-end training scheme, that leverages auxiliary data generated by LODEC or other radical-based encoding methods. We performed systematic experiments on both Chinese and Japanese datasets, and found that our approach surpassed the performance of state-of-the-art baselines. Furthermore, empirical evidence shows that our method can gain significantly better results using synthesized text line images without the need for domain knowledge.

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

Mengshi Zhang, Daniel Perelman, Vu Le, Sumit Gulwani

Responsive image

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

Slides Poster Similar

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.

Unsupervised deep learning for text line segmentation

Berat Kurar Barakat, Ahmad Droby, Reem Alaasam, Borak Madi, Irina Rabaev, Raed Shammes, Jihad El-Sana

Responsive image

Auto-TLDR; Unsupervised Deep Learning for Handwritten Text Line Segmentation without Annotation

Poster Similar

We present an unsupervised deep learning method for text line segmentation that is inspired by the relative variance between text lines and spaces among text lines. Handwritten text line segmentation is important for the efficiency of further processing. A common method is to train a deep learning network for embedding the document image into an image of blob lines that are tracing the text lines. Previous methods learned such embedding in a supervised manner, requiring the annotation of many document images. This paper presents an unsupervised embedding of document image patches without a need for annotations. The number of foreground pixels over the text lines is relatively different from the number of foreground pixels over the spaces among text lines. Generating similar and different pairs relying on this principle definitely leads to outliers. However, as the results show, the outliers do not harm the convergence and the network learns to discriminate the text lines from the spaces between text lines. Remarkably, with a challenging Arabic handwritten text line segmentation dataset, VML-AHTE, we achieved superior performance over the supervised methods. Additionally, the proposed method was evaluated on the ICDAR 2017 and ICFHR 2010 handwritten text line segmentation datasets.

Enhancing Handwritten Text Recognition with N-Gram Sequencedecomposition and Multitask Learning

Vasiliki Tassopoulou, George Retsinas, Petros Maragos

Responsive image

Auto-TLDR; Multi-task Learning for Handwritten Text Recognition

Slides Poster Similar

Current state-of-the-art approaches in the field of Handwritten Text Recognition are predominately single task with unigram, character level target units. In our work, we utilize a Multi-task Learning scheme, training the model to perform decompositions of the target sequence with target units of different granularity, from fine tocoarse. We consider this method as a way to utilize n-gram information, implicitly, in the training process, while the final recognition is performed using only the unigram output. Unigram decoding of sucha multi-task approach highlights the capability of the learned internal representations, imposed by the different n-grams at the training step. We select n-grams as our target units and we experiment from unigrams till fourgrams, namely subword level granularities.These multiple decompositions are learned from the network with task-specific CTC losses. Concerning network architectures, we pro-pose two alternatives, namely the Hierarchical and the Block Multi-task. Overall, our proposed model, even though evaluated only onthe unigram task, outperforms its counterpart single-task by absolute 2.52% WER and 1.02% CER, in the greedy decoding, without any computational overhead during inference, hinting towards success-fully imposing an implicit language model

ConvMath : A Convolutional Sequence Network for Mathematical Expression Recognition

Zuoyu Yan, Xiaode Zhang, Liangcai Gao, Ke Yuan, Zhi Tang

Responsive image

Auto-TLDR; Convolutional Sequence Modeling for Mathematical Expressions Recognition

Slides Poster Similar

Despite the recent advances in optical character recognition (OCR), mathematical expressions still face a great challenge to recognize due to their two-dimensional graphical layout. In this paper, we propose a convolutional sequence modeling network, ConvMath, which converts the mathematical expression description in an image into a LaTeX sequence in an end-to-end way. The network combines an image encoder for feature extraction and a convolutional decoder for sequence generation. Compared with other Long Short Term Memory(LSTM) based encoder-decoder models, ConvMath is entirely based on convolution, thus it is easy to perform parallel computation. Besides, the network adopts multi-layer attention mechanism in the decoder, which allows the model to align output symbols with source feature vectors automatically, and alleviates the problem of lacking coverage while training the model. The performance of ConvMath is evaluated on an open dataset named IM2LATEX-100K, including 103556 samples. The experimental results demonstrate that the proposed network achieves state-of-the-art accuracy and much better efficiency than previous methods.

Ancient Document Layout Analysis: Autoencoders Meet Sparse Coding

Homa Davoudi, Marco Fiorucci, Arianna Traviglia

Responsive image

Auto-TLDR; Unsupervised Unsupervised Representation Learning for Document Layout Analysis

Slides Poster Similar

Layout analysis of historical handwritten documents is a key pre-processing step in document image analysis that, by segmenting the image into its homogeneous regions, facilitates subsequent procedures such as optical character recognition and automatic transcription. Learning-based approaches have shown promising performances in layout analysis, however, the majority of them requires tedious pixel-wise labelled training data to achieve generalisation capabilities, this limitation preventing their application due to the lack of large labelled datasets. This paper proposes a novel unsupervised representation learning method for documents’ layout analysis that reduces the need for labelled data: a sparse autoencoder is first trained in an unsupervised manner on a historical text document’s image; representation of image patches, computed by the sparse encoder, is then used to classify pixels into various region categories of the document using a feed-forward neural network. A new training method, inspired by the ISTA algorithm, is also introduced here to train the sparse encoder. Experimental results on DIVA-HisDB dataset demonstrate that the proposed method outperforms previous approaches based on unsupervised representation learning while achieving performances comparable to the state-of-the-art fully supervised methods.

ID Documents Matching and Localization with Multi-Hypothesis Constraints

Guillaume Chiron, Nabil Ghanmi, Ahmad Montaser Awal

Responsive image

Auto-TLDR; Identity Document Localization in the Wild Using Multi-hypothesis Exploration

Slides Poster Similar

This paper presents an approach for spotting and accurately localizing identity documents in the wild. Contrary to blind solutions that often rely on borders and corners detection, the proposed approach requires a classification a priori along with a list of predefined models. The matching and accurate localization are performed using specific ID document features. This process is especially difficult due to the intrinsic variable nature of ID models (text fields, multi-pass printing with offset, unstable layouts, added artifacts, blinking security elements, non-rigid materials). We tackle the problem by putting different combinations of features in competition within a multi-hypothesis exploration where only the best document quadrilateral candidate is retained thanks to a custom visual similarity metric. The idea is to find, in a given context, at least one feature able to correctly crop the document. The proposed solution has been tested and has shown its benefits on both the MIDV-500 academic dataset and an industrial one supposedly more representative of a real-life application.

Multimodal Side-Tuning for Document Classification

Stefano Zingaro, Giuseppe Lisanti, Maurizio Gabbrielli

Responsive image

Auto-TLDR; Side-tuning for Multimodal Document Classification

Slides Poster Similar

In this paper, we propose to exploit the side-tuning framework for multimodal document classification. Side-tuning is a methodology for network adaptation recently introduced to solve some of the problems related to previous approaches. Thanks to this technique it is actually possible to overcome model rigidity and catastrophic forgetting of transfer learning by fine-tuning. The proposed solution uses off-the-shelf deep learning architectures leveraging the side-tuning framework to combine a base model with a tandem of two side networks. We show that side-tuning can be successfully employed also when different data sources are considered, e.g. text and images in document classification. The experimental results show that this approach pushes further the limit for document classification accuracy with respect to the state of the art.

A Transformer-Based Radical Analysis Network for Chinese Character Recognition

Chen Yang, Qing Wang, Jun Du, Jianshu Zhang, Changjie Wu, Jiaming Wang

Responsive image

Auto-TLDR; Transformer-based Radical Analysis Network for Chinese Character Recognition

Slides Poster Similar

Recently, a novel radical analysis network (RAN) has the capability of effectively recognizing unseen Chinese character classes and largely reducing the requirement of training data by treating a Chinese character as a hierarchical composition of radicals rather than a single character class.} However, when dealing with more challenging issues, such as the recognition of complicated characters, low-frequency character categories, and characters in natural scenes, RAN still has a lot of room for improvement. In this paper, we explore options to further improve the structure generalization and robustness capability of RAN with the Transformer architecture, which has achieved start-of-the-art results for many sequence-to-sequence tasks. More specifically, we propose to replace the original attention module in RAN with the transformer decoder, which is named as a transformer-based radical analysis network (RTN). The experimental results show that the proposed approach can significantly outperform the RAN on both printed Chinese character database and natural scene Chinese character database. Meanwhile, further analysis proves that RTN can be better generalized to complex samples and low-frequency characters, and has better robustness in recognizing Chinese characters with different attributes.

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

Responsive image

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

Slides Poster Similar

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.

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

Responsive image

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

Slides Poster Similar

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.

Automated Whiteboard Lecture Video Summarization by Content Region Detection and Representation

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

Responsive image

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

Poster Similar

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.

Combining Deep and Ad-Hoc Solutions to Localize Text Lines in Ancient Arabic Document Images

Olfa Mechi, Maroua Mehri, Rolf Ingold, Najoua Essoukri Ben Amara

Responsive image

Auto-TLDR; Text Line Localization in Ancient Handwritten Arabic Document Images using U-Net and Topological Structural Analysis

Slides Poster Similar

Text line localization in document images is still considered an open research task. The state-of-the-art methods in this regard that are only based on the classical image analysis techniques mostly have unsatisfactory performances especially when the document images i) contain significant degradations and different noise types and scanning defects, and ii) have touching and/or multi-skewed text lines or overlapping words/characters and non-uniform inter-line space. Moreover, localizing text in ancient handwritten Arabic document images is even more complex due to the morphological particularities related to the Arabic script. Thus, in this paper, we propose a hybrid method combining a deep network with classical document image analysis techniques for text line localization in ancient handwritten Arabic document images. The proposed method is firstly based on using the U-Net architecture to extract the main area covering the text core. Then, a modified RLSA combined with topological structural analysis are applied to localize whole text lines (including the ascender and descender components). To analyze the performance of the proposed method, a set of experiments has been conducted on many recent public and private datasets, and a thorough experimental evaluation has been carried out.

CDeC-Net: Composite Deformable Cascade Network for Table Detection in Document Images

Madhav Agarwal, Ajoy Mondal, C. V. Jawahar

Responsive image

Auto-TLDR; CDeC-Net: An End-to-End Trainable Deep Network for Detecting Tables in Document Images

Slides Similar

Localizing page elements/objects such as tables, figures, equations, etc. is the primary step in extracting information from document images. We propose a novel end-to-end trainable deep network, (CDeC-Net) for detecting tables present in the documents. The proposed network consists of a multistage extension of Mask R-CNN with a dual backbone having deformable convolution for detecting tables varying in scale with high detection accuracy at higher IoU threshold. We empirically evaluate CDeC-Net on all the publicly available benchmark datasets— ICDAR-2013, ICDAR-2017, ICDAR-2019, UNLV, Marmot, PubLayNet, TableBank, and IIIT-AR-13K —with extensive experiments. Our solution has three important properties:(i) a single trained model CDeC-Net‡ performs well across all the popular benchmark datasets; (ii) we report excellent performances across multiple, including higher, thresholds of IoU; (iii) by following the same protocol of the recent papers for each of the benchmarks, we consistently demonstrate the superior quantitative performance. Our code and models will be publicly released for enabling reproducibility of the results.

An Evaluation of DNN Architectures for Page Segmentation of Historical Newspapers

Manuel Burghardt, Bernhard Liebl

Responsive image

Auto-TLDR; Evaluation of Backbone Architectures for Optical Character Segmentation of Historical Documents

Slides Poster Similar

One important and particularly challenging step in the optical character recognition of historical documents with complex layouts, such as newspapers, is the separation of text from non-text content (e.g. page borders or illustrations). This step is commonly referred to as page segmentation. While various rule-based algorithms have been proposed, the applicability of Deep Neural Networks for this task recently has gained a lot of attention. In this paper, we perform a systematic evaluation of 11 different published backbone architectures and 9 different tiling and scaling configurations for separating text, tables or table column lines. We also show the influence of the number of labels and the number of training pages on the segmentation quality, which we measure using the Matthews Correlation Coefficient. Our results show that (depending on the task) Inception-ResNet-v2 and EfficientNet backbones work best, vertical tiling is generally preferable to other tiling approaches, and training data that comprises 30 to 40 pages will be sufficient most of the time.

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

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

Responsive image

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

Slides Poster Similar

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.

Named Entity Recognition and Relation Extraction with Graph Neural Networks in Semi Structured Documents

Manuel Carbonell, Pau Riba, Mauricio Villegas, Alicia Fornés, Josep Llados

Responsive image

Auto-TLDR; Graph Neural Network for Entity Recognition and Relation Extraction in Semi-Structured Documents

Slides Similar

The use of administrative documents to communicate and leave record of business information requires of methods able to automatically extract and understand the content from such documents in a robust and efficient way. In addition, the semi-structured nature of these reports is specially suited for the use of graph-based representations which are flexible enough to adapt to the deformations from the different document templates. Moreover, Graph Neural Networks provide the proper methodology to learn relations among the data elements in these documents. In this work we study the use of Graph Neural Network architectures to tackle the problem of entity recognition and relation extraction in semi-structured documents. Our approach achieves state of the art results on the three tasks involved in the process. Moreover, the experimentation with two datasets of different nature demonstrates the good generalization ability of our approach.

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

Dafeng Wei, Hongtao Lu, Yi Zhou, Kai Chen

Responsive image

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

Slides Poster Similar

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.

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

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

Responsive image

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

Slides Poster Similar

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.

TGCRBNW: A Dataset for Runner Bib Number Detection (and Recognition) in the Wild

Pablo Hernández-Carrascosa, Adrian Penate-Sanchez, Javier Lorenzo, David Freire Obregón, Modesto Castrillon

Responsive image

Auto-TLDR; Racing Bib Number Detection and Recognition in the Wild Using Faster R-CNN

Slides Poster Similar

Racing bib number (RBN) detection and recognition is a specific problem related to text recognition in natural scenes. In this paper, we present a novel dataset created after registering participants in a real ultrarunning competition which comprises a wide range of acquisition conditions in five different recording points, including nightlight and daylight. The dataset contains more than 3k samples of over 400 different individuals. The aim is at providing an in the wild benchmark for both RBN detection and recognition problems. To illustrate the present difficulties, the dataset is evaluated for RBN detection using different Faster R-CNN specific detection models, filtering its output with heuristics based on body detection to improve the overall detection performance. Initial results are promising, but there is still a significant room for improvement. And detection is just the first step to accomplish in the wild RBN recognition.

2D License Plate Recognition based on Automatic Perspective Rectification

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

Responsive image

Auto-TLDR; Perspective Rectification Network for License Plate Recognition

Slides Poster Similar

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.

Robust Lexicon-Free Confidence Prediction for Text Recognition

Qi Song, Qianyi Jiang, Rui Zhang, Xiaolin Wei

Responsive image

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

Slides Poster Similar

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.

Scene Text Detection with Selected Anchors

Anna Zhu, Hang Du, Shengwu Xiong

Responsive image

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

Slides Poster Similar

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.

Text Recognition in Real Scenarios with a Few Labeled Samples

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

Responsive image

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

Slides Poster Similar

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.

Cross-Lingual Text Image Recognition Via Multi-Task Sequence to Sequence Learning

Zhuo Chen, Fei Yin, Xu-Yao Zhang, Qing Yang, Cheng-Lin Liu

Responsive image

Auto-TLDR; Cross-Lingual Text Image Recognition with Multi-task Learning

Slides Poster Similar

This paper considers recognizing texts shown in a source language and translating into a target language, without generating the intermediate source language text image recognition results. We call this problem Cross-Lingual Text Image Recognition (CLTIR). To solve this problem, we propose a multi-task system containing a main task of CLTIR and an auxiliary task of Mono-Lingual Text Image Recognition (MLTIR) simultaneously. Two different sequence to sequence learning methods, a convolution based attention model and a BLSTM model with CTC, are adopted for these tasks respectively. We evaluate the system on a newly collected Chinese-English bilingual movie subtitle image dataset. Experimental results demonstrate the multi-task learning framework performs superiorly in both languages.

Multi-Task Learning Based Traditional Mongolian Words Recognition

Hongxi Wei, Hui Zhang, Jing Zhang, Kexin Liu

Responsive image

Auto-TLDR; Multi-task Learning for Mongolian Words Recognition

Slides Poster Similar

In this paper, a multi-task learning framework has been proposed for solving and improving traditional Mongolian words recognition. To be specific, a sequence-to-sequence model with attention mechanism was utilized to accomplish the task of recognition. Therein, the attention mechanism is designed to fulfill the task of glyph segmentation during the process of recognition. Although the glyph segmentation is an implicit operation, the information of glyph segmentation can be integrated into the process of recognition. After that, the two tasks can be accomplished simultaneously under the framework of multi-task learning. By this way, adjacent image frames can be decoded into a glyph more precisely, which results in improving not only the performance of words recognition but also the accuracy of character segmentation. Experimental results demonstrate that the proposed multi-task learning based scheme outperforms the conventional glyph segmentation-based method and various segmentation-free (i.e. holistic recognition) methods.

Stroke Based Posterior Attention for Online Handwritten Mathematical Expression Recognition

Changjie Wu, Qing Wang, Jianshu Zhang, Jun Du, Jiaming Wang, Jiajia Wu, Jin-Shui Hu

Responsive image

Auto-TLDR; Posterior Attention for Online Handwritten Mathematical Expression Recognition

Slides Poster Similar

Recently, many researches propose to employ attention based encoder-decoder models to convert a sequence of trajectory points into a LaTeX string for online handwritten mathematical expression recognition (OHMER), and the recognition performance of these models critically relies on the accuracy of the attention. In this paper, unlike previous methods which basically employ a soft attention model, we propose to employ a posterior attention model, which modifies the attention probabilities after observing the output probabilities generated by the soft attention model. In order to further improve the posterior attention mechanism, we propose a stroke average pooling layer to aggregate point-level features obtained from the encoder into stroke-level features. We argue that posterior attention is better to be implemented on stroke-level features than point-level features as the output probabilities generated by stroke is more convincing than generated by point, and we prove that through experimental analysis. Validated on the CROHME competition task, we demonstrate that stroke based posterior attention achieves expression recognition rates of 54.26% on CROHME 2014 and 51.75% on CROHME 2016. According to attention visualization analysis, we empirically demonstrate that the posterior attention mechanism can achieve better alignment accuracy than the soft attention mechanism.

Cross-People Mobile-Phone Based Airwriting Character Recognition

Yunzhe Li, Hui Zheng, He Zhu, Haojun Ai, Xiaowei Dong

Responsive image

Auto-TLDR; Cross-People Airwriting Recognition via Motion Sensor Signal via Deep Neural Network

Slides Poster Similar

Airwriting using mobile phones has many applications in human-computer interaction. However, the recognition of airwriting character needs a lot of training data from user, which brings great difficulties to the pratical application. The model learnt from a specific person often cannot yield satisfied results when used on another person. The data gap between people is mainly caused by the following factors: personal writing styles, mobile phone sensors, and ways to hold mobile phones. To address the cross-people problem, we propose a deep neural network(DNN) that combines convolutional neural network(CNN) and bilateral long short-term memory(BLSTM). In each layer of the network, we also add an AdaBN layer which is able to increase the generalization ability of the DNN. Different from the original AdaBN method, we explore the feasibility for semi-supervised learning. We implement it to our design and conduct comprehensive experiments. The evaluation results show that our system can achieve an accuracy of 99% for recognition and an improvement of 10% on average for transfer learning between various factors such as people, devices and postures. To the best of our knowledge, our work is the first to implement cross-people airwriting recognition via motion sensor signal, which is a fundamental step towards ubiquitous sensing.

Segmenting Messy Text: Detecting Boundaries in Text Derived from Historical Newspaper Images

Carol Anderson, Phil Crone

Responsive image

Auto-TLDR; Text Segmentation of Marriage Announcements Using Deep Learning-based Models

Slides Poster Similar

Text segmentation, the task of dividing a document into sections, is often a prerequisite for performing additional natural language processing tasks. Existing text segmentation methods have typically been developed and tested using clean, narrative-style text with segments containing distinct topics. Here we consider a challenging text segmentation task: dividing newspaper marriage announcement lists into units of one couple each. In many cases the information is not structured into sentences, and adjacent segments are not topically distinct from each other. In addition, the text of the announcements, which is derived from images of historical newspapers via optical character recognition, contains many typographical errors. Because of these properties, these announcements are not amenable to segmentation with existing techniques. We present a novel deep learning-based model for segmenting such text and show that it significantly outperforms an existing state-of-the-art method on our task.

Detecting Objects with High Object Region Percentage

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

Responsive image

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

Slides Poster Similar

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.

Large-Scale Historical Watermark Recognition: Dataset and a New Consistency-Based Approach

Xi Shen, Ilaria Pastrolin, Oumayma Bounou, Spyros Gidaris, Marc Smith, Olivier Poncet, Mathieu Aubry

Responsive image

Auto-TLDR; Historical Watermark Recognition with Fine-Grained Cross-Domain One-Shot Instance Recognition

Slides Poster Similar

Historical watermark recognition is a highly practical, yet unsolved challenge for archivists and historians. With a large number of well-defined classes, cluttered and noisy samples, different types of representations, both subtle differences between classes and high intra-class variation, historical watermarks are also challenging for pattern recognition. In this paper, overcoming the difficulty of data collection, we present a large public dataset with more than 6k new photographs, allowing for the first time to tackle at scale the scenarios of practical interest for scholars: one-shot instance recognition and cross-domain one-shot instance recognition amongst more than 16k fine-grained classes. We demonstrate that this new dataset is large enough to train modern deep learning approaches, and show that standard methods can be improved considerably by using mid-level deep features. More precisely, we design both a matching score and a feature fine-tuning strategy based on filtering local matches using spatial consistency. This consistency-based approach provides important performance boost compared to strong baselines. Our model achieves 55\% as top-1 accuracy on our very challenging 16,753-class one-shot cross-domain recognition task, each class described by a single drawing from the classic Briquet catalog. In addition to watermark classification, we show our approach provides promising results on fine-grained sketch-based image retrieval.

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

Pan Gao, Qi Wan, Renwu Gao, Linlin Shen

Responsive image

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

Slides Poster Similar

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.

Iterative Bounding Box Annotation for Object Detection

Bishwo Adhikari, Heikki Juhani Huttunen

Responsive image

Auto-TLDR; Semi-Automatic Bounding Box Annotation for Object Detection in Digital Images

Slides Poster Similar

Manual annotation of bounding boxes for object detection in digital images is tedious, and time and resource consuming. In this paper, we propose a semi-automatic method for efficient bounding box annotation. The method trains the object detector iteratively on small batches of labeled images and learns to propose bounding boxes for the next batch, after which the human annotator only needs to correct possible errors. We propose an experimental setup for simulating the human actions and use it for comparing different iteration strategies, such as the order in which the data is presented to the annotator. We experiment on our method with three datasets and show that it can reduce the human annotation effort significantly, saving up to 75% of total manual annotation work.