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Ancient Chinese character recognition is a challenging problem in the field of pattern recognition. It is difficult to collect all character classes during the training stage due to the numerous classes of ancient Chinese characters and the likelihood of discovering new characters over time. A solution to address this problem is continual learning. However, most continual learning methods are not well-suited for large-scale applications, making them insufficient for solving the problem of ancient Chinese character recognition. Although saving raw data for old classes is a good approach for continual learning to address large-scale problems, it is often infeasible due to the lack of data accessibility in reality. To solve these problems, we propose a large-scale continual learning framework based on the convolutional prototype network (CPN), which does not save raw data for old classes. In this paper, several basic strategies have been proposed for the initial training stage to enhance the feature extraction ability and robustness of the network, which can improve the performance of the model in continual learning. In addition, we propose two practical methods in varying feature space (parameters of feature extractor are changeable) and fixed feature space (parameters of feature extractor are fixed), which enable the model to carry out large-scale continual learning. The proposed method does not save the raw data of old classes and enables simultaneous classification of all existing classes without knowing the incremental batch number. Experiments on the CASIA-AHCDB dataset with 5000 character classes demonstrate the effectiveness and superiority of the proposed method. • We propose a prototype-based framework for large-scale continual learning. • The proposed strategies in the initial stage are good for continual learning. • We propose two continual learning methods in varying and fixed feature space. • The proposed methods achieve SOTA results on the CASIA-AHCDB dataset with 5000 classes. [ABSTRACT FROM AUTHOR]

Recent deep learning based methods have achieved the state-of-the-art performance for handwritten Chinese character recognition (HCCR) by learning discriminative representations directly from raw data. Nevertheless, we believe that the long-and-well investigated domain-specific knowledge should still help to boost the performance of HCCR. By integrating the traditional normalization-cooperated direction-decomposed feature map (directMap) with the deep convolutional neural network (convNet), we are able to obtain new highest accuracies for both online and offline HCCR on the ICDAR-2013 competition database. With this new framework, we can eliminate the needs for data augmentation and model ensemble, which are widely used in other systems to achieve their best results. This makes our framework to be efficient and effective for both training and testing. Furthermore, although directMap+convNet can achieve the best results and surpass human-level performance, we show that writer adaptation in this case is still effective. A new adaptation layer is proposed to reduce the mismatch between training and test data on a particular source layer. The adaptation process can be efficiently and effectively implemented in an unsupervised manner. By adding the adaptation layer into the pre-trained convNet, it can adapt to the new handwriting styles of particular writers, and the recognition accuracy can be further improved consistently and significantly. This paper gives an overview and comparison of recent deep learning based approaches for HCCR, and also sets new benchmarks for both online and offline HCCR. [ABSTRACT FROM AUTHOR]

• We extend the cross-modal prototype learning CMPL framework to three modalities. • CMPL achieves state-of-the-art results on online and offline zero-shot handwritten character recognition. • CMPL shows promising cross-domain generalization ability in zero-shot handwritten character recognition. Traditional methods of handwritten character recognition rely on extensive labeled data. However, humans can generalize to unseen handwritten characters by watching a few printed examples in textbooks. To simulate this ability, we propose a cross-modal prototype learning method (CMPL) to realize zero-shot recognition. For each character class, a prototype is generated by mapping the printed character into a deep neural network feature space. For unseen character class, its prototype can be directly produced from a printed character sample, therefore, not requiring any handwritten samples to realize class-incremental learning. Specifically, CMPL considers different modalities simultaneously - online handwritten trajectories, offline handwritten images, and auxiliary printed character images. The joint learning of the above modalities is achieved through sharing printed prototypes between online and offline data. In zero-shot inference, we feed CMPL the printed samples to obtain corresponding class prototypes, and then the unseen handwritten character can be recognized by the nearest prototype. Our experimental results demonstrate that CMPL outperforms the state-of-the-art methods in both online and offline zero-shot handwritten Chinese character recognition. Moreover, we also show the cross-domain generalization of CMPL from two perspectives: cross-language and modern-to-ancient handwritten character recognition, focusing on the transferability between different languages and different styles (i.e., modern and historical handwritings). [ABSTRACT FROM AUTHOR]

• We propose the self-information of radicals (SIR) from the information theory perspective to measure the importance of radicals in recognizing Chinese characters. • The proposed SIR can be easily adopted by two commonly used radical-based zero-shot Chinese Character Recognition (ZSCCR) frameworks, i.e., sequence matching based and attribute embedding based. • For sequence matching based ZSCCR, we propose a novel Chinese character uncertainty elimination (CUE) framework, which is capable of alleviating the sequence mismatch problem. • For attribute embedding based ZSCCR, we propose a novel radical information embedding (RIE) method, which can highlight the importance of indispensable radicals. • Comprehensive experiments on the CASIA-HWDB, ICDAR2013, CTW, and AHCDB datasets demonstrate the effectiveness and high extensibility of the proposed SIR. Zero-shot Chinese character recognition (ZSCCR) is an important research topic in Chinese character recognition as it attempts to recognize unseen Chinese characters. As basic components and mid-level representations, radicals are significant for ZSCCR. However, previous methods treat the importance of radicals equally, ignoring the different contributions of radicals in distinguishing characters. In this paper, we propose the self-information of radicals (SIR) to measure the importance of radicals in recognizing Chinese characters. The proposed SIR can be easily adopted by two commonly used radical-based ZSCCR frameworks, i.e., sequence matching based and attribute embedding based. For sequence matching based ZSCCR, we propose a novel Chinese character uncertainty elimination (CUE) framework to alleviate the radical sequence mismatch problem. For attribute embedding based ZSCCR, we propose a novel radical information embedding (RIE) method that can highlight the importance of indispensable radicals and weaken the influence of some unnecessary radicals. We conducted comprehensive experiments on the CASIA-HWDB, ICDAR2013, CTW datasets, and AHCDB datasets to evaluate the proposed method. Experiments show that our proposed methods can achieve superior performance to the state-of-the-art methods, which demonstrate the effectiveness and the high extensibility of the proposed SIR. [ABSTRACT FROM AUTHOR]