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.

Chinese character recognition has attracted much interest due to its high challenge and various applications. The whole-character modeling method can recognize common characters well but unable to handle unseen situation. Some radical-based modeling methods have successfully achieved great performance in unseen condition but the decoding takes huge time comsumption. Therefore, a high-efficient model which can recognize unseen characters needs to be proposed. First, this paper introduces a novel radical counter network (RCN) to recognize Chinese characters by identifying radicals and spatial structures. The proposed RCN first extracts visual features from input by employing DenseNet as encoder. Then a decoder based on fully connected layer is employed, aiming at synchronously estimating the number of each caption in character. The manner of simultaneously decoding all the captions greatly saves time of sequence decoding. Additionally, we design a multi-task learning to combine global feature extraction capability of whole-character modeling and local feature extraction capability of radical-based modeling, which further improves the model generalization. Experiments on natural scene character dataset demonstrate that the proposed model significantly outperforms baseline by 4.81\% with a comparable model complexity. That shows great robustness and simplicity of our model.

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.