Recognizing online handwritten Chinese characters using RNNs with new computing architectures

This paper presents a new approach to large-vocabulary online handwritten Chinese character recognition based on semi-tied covariance (STC) modeling. Detailed procedures are described for estimating the STC model parameters under both maximum likelihood (ML) and minimum classification error (MCE) criteria. Compared with the state-of-the-art modified quadratic discriminant function (MQDF) based classifiers, STC-based classifiers can achieve a better memory-accuracy trade-off, thus provide more flexibility in designing compact online handwritten Chinese character recognizers. Its usefulness has been confirmed and demonstrated by comparative experiments on popular Nakayosi and Kuchibue Japanese character databases.

A new perspective: Recognizing online handwritten Chinese characters via 1-dimensional CNN

Handwritten Chinese character recognition (HCCR) is an important research field of pattern recognition, which has attracted extensive studies during the past decades. Recently convolutional neural network (CNN) based methods have achieved the state-of-the-art performance for handwritten Chinese character recognition. Nevertheless, handwritten Chinese character recognition is still limited to be effectively used in the actual environment due to the large-scale vocabulary and great diversity of handwriting style. In this paper, we constructed a handwritten Chinese character recognition service based on convolutional neural network, which tries to make effective use of handwritten based printed fonts and existing handwritten database. At the same time, the service can effectively collect more handwritten data to expand the training dataset, which makes it easy to adapt to the new handwriting styles. Meanwhile, We propose a multi-level recognition theory applied to online handwritten Chinese character recognition, which may improve the accuracy of handwritten Chinese character recognition and break the limitations of handwritten Chinese character recognition by identifying the structure of Chinese characters and possible stroke orders firstly. Furthermore, we try to apply the method of online character recognition to the offline character recognition based on the basic writing rules.

This paper proposes an on-line handwritten Chinese character recognition system based on stroke-sequence feature extraction. The character to be recognized can be stroke-order and stroke-number free, tolerance for combined strokes, size flexible, but within the constraint of normal hand-writing. Firstly, the recognizer, using the finite state matching mechanism, is used to extract primitive strokes, represented as stroke string, from the input character. Secondly, the recognizer, using a modified dynamic programming matching method, is employed to perform recognition processes with the stroke-string features. Reference patterns have been generated 2500 Chinese characters with stroke-numbers ranging from 1 to 29. The recognition results are based upon the 1800 handwritten characters by 10 people. The obtained recognition rate is 94.5%, and the cumulative classification rate of choosing fourth most similar characters is up to 98.7%. In the last part, a secondary recognition mechanism is used to further tell apart the candidates involved. The final recognition rate may be promoted up to 99%.

This paper presents a new linguistic decoding method for online handwritten Chinese character recognition. The method employs a hybrid language model which combinesN-gram and linguistic rules by rule quantification technique. The linguistic decoding algorithm consists of three stages: word lattice construction, the optimal sentence hypothesis search and self-adaptive learning mechanism. The technique has been applied to palmtop computer’s online handwritten Chinese character recognition. Samples containing millions of characters were used to test the linguistic decoder. In the open experiment, accuracy rate up to 92% is achieved, and the error rate is reduced by 68%.

This paper presents a novel approach to writer adaptation using bottleneck features and discriminative linear regression for the recognition of online handwritten Chinese characters. First, bottleneck features extracted from a bottleneck layer of a deep neural network representing a nonlinear and discriminative transformation of the input features are verified to be much more effective in adaptation of writing styles than the conventional features after linear discriminant analysis transformation. Second, discriminative linear regression via a so-called sample separation margin based minimum classification error criterion is adopted for writer adaptation. The experiments on an in-house developed online Chinese handwriting corpus with a vocabulary of 15,167 characters and testing data collected from user inputs of Smartphones show that our proposed approach can achieve very significant improvements of recognition accuracy compared with a state-of-the-art adaptation approach for writer adaptation.

Due to the various individual handwriting styles and diverse structures of Chinese characters, it is rather intricate to recognize handwritten Chinese characters precisely. In this paper, a muti-stage system that combines a Differentiable Binarization Network (DBNet) for text detection and Scene Text Recognition with a Single Visual Model (SVTR) for effective character recognition is developed, aiming to offer a potential solution for correct identification of handwritten Chinese characters in real-world scenarios. More specifically, the proposed system utilized the CASIA-HWDB2.x dataset from the Institute of Automation of the Chinese Academy of Sciences to train text detection and recognition models. For the detection, the Differentiable Binarization method in the Head Network applies a dynamic threshold map for text-background differentiation. On top of the SVTR employed as a recognition module, this research proposed a performance-optimized PP-LCNet architecture and the Guided Training of Continuous Time-Series Classifiers (GTC) with TextConAug, which is a data augmentation strategy to accommodate real-world scenarios. Briefly, DBNet and SVTR are integrated to form an end-to-end system. The output of DBNet after undergoing cropping and direction classifier, guides SVTR for a comprehensive system, ensuring seamless detection and recognition of handwritten Chinese characters. Experimental results indicate that this system achieves a promising performance and can provide accurate information on handwritten Chinese characters.

In various application areas of pattern recognition, combining multiple classifiers is regarded as a method for achieving a substantial gain in performance of systems. The paper presents a method for handwritten Chinese character recognition to combine multiple classifiers based on statistics. Fusion strategies are discussed for providing a basis for combining classifiers. These combination strategies are experimentally tested on an online handwritten Chinese character recognition system. In our experiments, other combination approaches are also involved for comparison.

Hierarchical attributed graph representation and recognition of handwritten chinese characters

The task of handwritten Chinese character recognition is one of the most challenging areas of human handwriting classification. The main reason for this is related to the writing system itself which encompasses thousands of characters, coupled with high levels of diversity in personal writing styles and attributes. Much of the existing work for both online and off-line handwritten Chinese character recognition has focused on methods which employ feature extraction and segmentation steps. The preprocessed data from these steps form the basis for the subsequent classification and recognition phases. This paper proposes an approach for handwritten Chinese character recognition and classification using only an image alignment technique and does not require the aforementioned steps. Rather than extracting features from the image, which often means building models from very large training data, the proposed method instead uses the mean image transformations as a basis for model building. The use of an image-only model means that no subjective tuning of the feature extraction is required. In addition by employing a fuzzy-entropy-based metric, the work also entails improved ability to model different types of uncertainty. The classifier is a simple distance-based nearest neighbour classification system based on template matching. The approach is applied to a publicly available real-world database of handwritten Chinese characters and demonstrates that it can achieve high classification accuracy and is robust in the presence of noise.

This paper proposes a hidden Markov mesh random field (HMMRF)-based stochastic model for off-line handwritten Chinese characters recognition using statistical observation sequences embedded in the strokes of a character. Due to the great amount of Chinese characters and many different writing styles or variations, the recognition of handwritten Chinese characters becomes more difficult and challenging than any other character recognition. In our approach, a new framework based on HMMRF model is put forward at first. The estimation of model parameters and state sequence decoding algorithms are also discussed later. Besides the mathematical model and corresponding issues, nonlinear shape normalization scheme that modifies the distortion and adjusts the correlation of strokes is applied. Two types of stroke-based features are extracted for the rough classification and observation sequence respectively. Experimental results on isolated handwritten Chinese characters demonstrate the effectiveness of our approach.

Recognition of handwritten Chinese characters by elastic matching

This paper describes a new feature extraction method contributing to improvement of the performance of a handwritten Chinese character recognition system. By using enhanced weighted dynamic meshes based on nonlinear normalization, this method not only avoids the zigzags and other undesirable side effects introduced in the original Yamada et al.’s nonlinear normalization method but also avoids additional feature normalization process in the original Lian-Wen Jin et al.’s and WU Tian-lei et al.’s dynamic mesh method. Experiment on HCL2000, a handwritten Chinese character database, shows that our method achieves superior performance.KeywordsChinese CharacterCharacter RecognitionFeature Extraction MethodDynamic MeshHandwritten CharacterThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The long-standing challenges for offline handwritten Chinese character recognition (HCCR) are twofold: Chinese characters can be very diverse and complicated while similarly looking, and cursive handwriting (due to increased writing speed and infrequent pen lifting) makes strokes and even characters connected together in a flowing manner. In this paper, we propose the template and instance loss functions for the relevant machine learning tasks in offline handwritten Chinese character recognition. First, the character template is designed to deal with the intrinsic similarities among Chinese characters. Second, the instance loss can reduce category variance according to classification difficulty, giving a large penalty to the outlier instance of handwritten Chinese character. Trained with the new loss functions using our deep network architecture HCCR14Layer model consisting of simple layers, our extensive experiments show that it yields state-of-the-art performance and beyond for offline HCCR.

This paper presents a methodology for off-line handwritten Chinese character recognition based on mergence of consecutive segments of adaptive duration. The handwritten Chinese character string is partitioned into a sequence of consecutive segments, which are combined to implement dissimilarity evaluation within a sliding window whose durations are determined adaptively by the integration of shapes and context of evaluations. The average stroke width is estimated for the handwritten Chinese character string, and a set of candidate character segmentation boundaries is found by using the integration of pixel and stroke features. The final decisions on segmentation and recognition are made under minimal arithmetical mean dissimilarities. Experiments proved that the proposed approach of adaptive duration outperforms the method of fixed duration, and is very effective for the recognition of overlapped, broken, touched, loosely configured Chinese characters.