Handwritten mathematical expression recognition (HMER) is an essential task in the OCR community, which consists of two sub-tasks, i.e., symbol recognition and structure parsing. Modern literature treats HMER as a LaTeX sequence predicting problem that simultaneously recognizes symbols and parses the structures of MEs. Although deep learning-based HMER methods have been achieving promising results on public benchmarks, it is admitted that the misclassification error between visually similar symbols still prevents these approaches from more generalized scenes. In this paper, we try to solve this issue from three aspects. 1) We enhanced the feature extraction progress by introducing path signature features, which incorporates local writing details and global spatial information. 2) We developed a language model that uses contextual information to correct the symbols misclassified by vision-only-based recognition models. 3) We solved the misalignment problem in existing ensemble method by designing a dynamic time warping (DTW) based algorithm. By combining the above improvements, our method achieved state-of-the-art results on three CROHME benchmarks, outperforming previous methods by a large margin.

Handwritten Mathematical Expression Recognition (HMER) plays a critical role in various applications, such as digitized education and scientific research. Although existing methods have achieved promising performance on publicly available datasets, they still struggle to recognize multi-line mathematical expressions (MEs), suffering from complex structures and slow inference speed. To address these issues, we propose a Line-Aware Semi-autoregressive Transformer (LAST) that treats multi-line mathematical expression sequences as two-dimensional dual-end structures. The proposed LAST utilizes a line-wise dual-end decoding strategy to decode multi-line mathematical expressions in parallel and perform dual-end decoding within each line. Specifically, we introduce a line-aware positional encoding module and a line-partitioned dual-end mask to endow LAST with line order awareness and directionality. Additionally, we adopt a shared-task optimization strategy to train LAST in both autoregressive and semi-autoregressive tasks. To evaluate the effectiveness of our approach in real-world scenarios, we have built a new Multi-line Mathematical Expression dataset (M2E), which, to the best of our knowledge, is the first of its kind and boasts with the largest character category, the largest samples of characters, and the longest average sequence length, compared to existing ME datasets. Experimental results on both the M2E dataset and publicly available datasets demonstrate the effectiveness of our proposed method. Notably, our semi-autoregressive decoding approach achieves significantly faster decoding speeds while still achieving state-of-the-art performance compared to the existing methods.

Large amounts of labeled data are urgently required for the training of robust text recognizers. However, collecting handwriting data of diverse styles, along with an immense lexicon, is considerably expensive. Although data synthesis is a promising way to relieve data hunger, two key issues of handwriting synthesis, namely, style representation and content embedding, remain unsolved. To this end, we propose a novel method that can synthesize parameterized and controllable handwriting S tyles for arbitrary-Length and O ut-of-vocabulary text based on a G enerative A dversarial N etwork (GAN), termed SLOGAN. Specifically, we propose a style bank to parameterize specific handwriting styles as latent vectors, which are input to a generator as style priors to achieve the corresponding handwritten styles. The training of the style bank requires only writer identification of the source images, rather than attribute annotations. Moreover, we embed the text content by providing an easily obtainable printed style image, so that the diversity of the content can be flexibly achieved by changing the input printed image. Finally, the generator is guided by dual discriminators to handle both the handwriting characteristics that appear as separated characters and in a series of cursive joins. Our method can synthesize words that are not included in the training vocabulary and with various new styles. Extensive experiments have shown that high-quality text images with great style diversity and rich vocabulary can be synthesized using our method, thereby enhancing the robustness of the recognizer.

Handwritten signature verification is a challenging task because signatures of a writer may be skillfully imitated by a forger. As skilled forgeries are generally difficult to acquire for training, in this paper, we propose a deep learning-based dynamic signature verification framework, SynSig2Vec, to address the skilled forgery attack without training with any skilled forgeries. Specifically, SynSig2Vec consists of a novel learning-by-synthesis method for training and a 1D convolutional neural network model, called Sig2Vec, for signature representation extraction. The learning-by-synthesis method first applies the Sigma Lognormal model to synthesize signatures with different distortion levels for genuine template signatures, and then learns to rank these synthesized samples in a learnable representation space based on average precision optimization. The representation space is achieved by the proposed Sig2Vec model, which is designed to extract fixed-length representations from dynamic signatures of arbitrary lengths. Through this training method, the Sig2Vec model can extract extremely effective signature representations for verification. Our SynSig2Vec framework requires only genuine signatures for training, yet achieves state-of-the-art performance on the largest dynamic signature database to date, DeepSignDB, in both skilled forgery and random forgery scenarios. Source codes of SynSig2Vec will be available at https://github.com/LaiSongxuan/SynSig2Vec.

Text recognition is a popular research subject with many associated challenges. Despite the considerable progress made in recent years, the text recognition task itself is still constrained to solve the problem of reading cropped line text images and serves as a subtask of optical character recognition (OCR) systems. As a result, the final text recognition result is limited by the performance of the text detector. In this paper, we propose a simple, elegant and effective paradigm called Implicit Feature Alignment (IFA), which can be easily integrated into current text recognizers, resulting in a novel inference mechanism called IFA- inference. This enables an ordinary text recognizer to process multi-line text such that text detection can be completely freed. Specifically, we integrate IFA into the two most prevailing text recognition streams (attention-based and CTC-based) and propose attention-guided dense prediction (ADP) and Extended CTC (ExCTC). Furthermore, the Wasserstein-based Hollow Aggregation Cross-Entropy (WH-ACE) is proposed to suppress negative predictions to assist in training ADP and ExCTC. We experimentally demonstrate that IFA achieves state-of-the-art performance on end-to-end document recognition tasks while maintaining the fastest speed, and ADP and ExCTC complement each other on the perspective of different application scenarios. Code will be available at https://github.com/Wang-Tianwei/Implicit-feature-alignment.

Handwritten mathematical expression recognition (HMER) is an important research direction in handwriting recognition. The performance of HMER suffers from the two-dimensional structure of mathematical expressions (MEs). To address this issue, in this paper, we propose a high-performance HMER model with scale augmentation and drop attention. Specifically, tackling ME with unstable scale in both horizontal and vertical directions, scale augmentation improves the performance of the model on MEs of various scales. An attention-based encoder-decoder network is used for extracting features and generating predictions. In addition, drop attention is proposed to further improve performance when the attention distribution of the decoder is not precise. Compared with previous methods, our method achieves state-of-the-art performance on two public datasets of CROHME 2014 and CROHME 2016.

Offline signature verification is a challenging task, where the scarcity of the signature data per writer makes it a few-shot problem. We found that previous deep metric learning based methods, whether in pairs or triplets, are unaware of intra-writer variations and have low training efficiency because only point-to-point (P2P) distances are considered. To address this issue, we present a novel point-to-set (P2S) metric for offline signature verification in this paper. By dividing a training batch into a support set and a query set, our optimization goal is to pull each query to its belonging support set. To further strengthen the P2S metric, a hard mining scheme and a margin strategy are introduced. Experiments conducted on three datasets show the effectiveness of our proposed method.

Offline handwritten Chinese character recognition has attracted much interest due to its various applications. The most cutting-edge methods treat Chinese character as a whole, ignoring the structures and radicals that compose characters. To use the radical-level composition of Chinese characters and achieve few-shot/zero-shot Chinese character recognition, some methods attempt to recognize Chinese characters at the radical level; however, these methods have shown poor performance due to weak radical feature representation and the use of inflexible decoding algorithm. In this paper, a novel radical aggregation network (RAN) is proposed for few-shot/zero-shot offline handwritten Chinese character recognition. The RAN consists of three components, a radical mapping encoder (RME), a radical aggregation module (RAM), and a character analysis decoder (CAD). Experiments show that our method can effectively recognize unseen handwritten characters given few support samples, while maintaining a high performance on seen characters.

Handwritten Chinese character recognition (HCCR) is a major research area in pattern recognition. Although recent years have seen tremendous improvement in the use of deep neural networks in handwriting recognition, unconstrained handwriting recognition remains an open problem. Considering the problem of rotated handwritten Chinese character recognition, we propose a two-stage convolutional neural network combined with path signature features to achieve high-accuracy rotation-free HCCR. For handwritten Chinese characters whose rotation angle is up to ±45°, the recognition rate can reach 97.38% on the ICDAR-2013 online HCCR competition dataset, which is comparable to the accuracy achieved by several state-of-the-art methods for non-rotated characters, thus showing the effectiveness of the proposed method.