Handwritten Chinese character identification with Bagged One-Class support vector machines

Amongst all the methods of machine learning, learning from examples is considered as a key to acquire knowledge automatically. Learning from examples is to obtain a general cover through induction from a given set of positive and negative examples of a concept, which may describe all the positive examples, but reject all the negative examples of that concept. Extension matrix theory, a branch of learning from examples, presents the training examples as two matrices which consist of positive and negative examples respectively, and then finds a path satisfied with a positive example against the background of negative examples. Obviously, such a path is a conjunction of conditions that is satisfied by the positive example. However, most of all of pattern recognition problems, such as handwritten Chinese character recognition, have an overlay area. It is necessary to solve the problem of nonlinear boundaries among different classes when machine learning is applied to acquire the recognition rules. An advanced extension matrix algorithm is proposed in this paper, in which a heuristic search based on the average entropy is used to get the approximate solutions of the minimal complex. A potential function is used to estimate the probability density function of the overlay area between positive and negative examples, so that the nonlinear interfaces of the interclass areas may be obtained. This algorithm is applied to supervised learning for Chinese character recognition to acquire recognition rules, and the implementations upon 751,000 loosely-constrained handwritten Chinese characters indicate that these methodologies can be applied to a practical recognition system with promising results.

In this paper, the off-line Chinese character image is transformed into ellipse shape of basic Chinese characters strokes in different position. The stroke "turning" and joint or crossover of strokes is combined by basic strokes. The neural network for extracting Chinese character has been build up. The research on off-line Chinese character image is transformed into the research on double weights elliptical neural network based on biomimetic pattern. By extracting fault-tolerant features of the 4 kinds of basic stroke, 13 kinds of "turning" stroke and 7 kinds of consistency and intersection of strokes, the data-knowledge table of features is constructed. The method of bionic recognition gives the machine the ability of cognizing features of stroke type and number, features of stroke location and features of topology structural. The simple and complex handwritten Chinese characters are used to carry out simulation experiment in SCUT-IRAC-HCCLIB. The experiment results show that the algorithm exhibits a strong ability of cognizing handwritten Chinese characters.

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.

Applied Biomimetic Pattern Recognition to replace “differentiation” of characteristic sample by machine “cognition”, a novel method of handwritten Chinese characters recognition is presented. A double weights elliptical neuron is used to cover four basic kinds of handwritten Chinese characters stroke segment. The topological property among the stroke segment neurons is analyzed. Nine style of Chinese characters stroke with fault tolerance are combined. Imitated typing methods of human Chinese characters font code, the style and number of stroke neurons which have redundant fault tolerant shapes are counted. A kind of characteristic knowledge data-base table of handwritten Chinese characters and the sample data-base of standard printed Chinese characters and fault tolerant matching rules are built. Simple and more complex handwritten Chinese characters in SCUT-IRAC database are tested. The method is proved to be close to human fault tolerance and veracity.

Under the current educational background of “double subtraction in order to effectively improve students’ ability to write standardized Chinese characters and solve the problem of shortage of teachers for writing education, an intelligent scoring method for standardized Chinese character writing based on template Chinese character eigenvalue similarity is proposed. The method consists of three steps: firstly, a Chinese character evaluation classification model is established based on the eigenvalue information of handwritten Chinese character samples and expert pre-evaluation results, and the standard interval of Chinese character eigenvalue is determined based on the classification results of the model. Secondly, a multiple linear regression model is established based on the scores of individual writing rules of Chinese characters and the overall scores of experts on handwritten Chinese character samples. Through the model, the influence weight of each writing rule on the evaluation of whole character writing is determined. Thirdly, combining the similarity of eigenvalues, the difference of eigenvalues and the influence weight ofwriting rules, we can score the handwritten Chinese characters, include overall score and quantifiable details.

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.

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.

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.

To realize intelligent teaching of Chinese characters handwriting, an ex-post evaluation technique about the correctness of handwritten Chinese characters is proposed. The basic idea contains two steps: firstly, the font matching is performed between the handwritten characters and template characters; then, feedback on the writing errors in the handwritten characters is given according to the matching results. Regarding the aspect of font matching, the relaxation matching algorithm is adopted and improved in this paper, which can obtain an accurate font correspondence at the level of stroke segment. Regarding the aspect of error feedback, we integrate the local errors accumulated in the handwritten characters, and present an appropriate feedback scale. Experiments indicate that this technique has excellent stability and wide applicability.

Chinese characters are constructed by basic strokes based on structural rules. In handwritten characters, the shapes of the strokes may vary to some extent, but the spatial relations and geometric configurations of the strokes are usually maintained. Therefore these spatial relations and configurations could be regarded as invariant features and could be used in the recognition of handwritten Chinese characters. In this paper, we investigate the structural knowledge in Chinese characters and propose the stroke spatial relationship representation (SSRR) to describe Chinese characters. An On-Line Chinese Character Recognition (OLCCR) method using the SSRR is also presented. With SSRR, each character is processed and is represented by an attribute graph. The process of character recognition is thereby transformed into a graph matching problem. After careful analysis, the basic spatial relationship between strokes can be characterized into five classes. A bitwise representation is adopted in the design of the data structure to reduce storage requirements and to speed up character matching. The strategy of hierarchical search in the preclassification improves the recognition speed. Basically, the attribute graph model is a generalized character representation that provides a useful and convenient representation for newly added characters in an OLCCR system with automatic learning capability. The significance of the structural approach of character recognition using spatial relationships is analyzed and is proved by experiments. Realistic testing is provided to show the effectiveness of the proposed method.

Hierarchical attributed graph representation and recognition of handwritten chinese characters

A unique stroke ordering for handwritten Chinese characters is desirable in many applications including efficient character recognition and automatic radical extraction. Although there are some rules or conventions for writing Chinese characters, yet no consistent and complete rule set is available. Besides special radical knowledge is often needed. It is the purpose of this paper to propose a set of rules for stroke ordering for producing a unique stroke sequence for Chinese characters. It requires no special radical knowledge or knowledge of character block layouts, so it is easy for machine implementation. Moreover, the stroke sequences derived are similar to those given in the dictionary, if not the same. To deal with the writing, variations among writers, we generalize the derived stroke structure sequence to obtain a more consistent stroke information. This generalized stroke structural sequence can be used in the handwritten Chinese character preclassification. Experiments showing applications of our method are reported.

A key property of Chinese characters is that they are composed of fundamental parts called radicals. In this paper, a method to recognise (offline) the radicals of handwritten Chinese characters is proposed that is an extension of the authors' previous work based on active shape modelling. Three stages are involved: a set of example radicals is first described by landmarks using (mostly) automatic landmark labelling, then radicals are modelled as active shapes using kernel principal component analysis, and finally unseen radicals are matched to the reference models using a genetic algorithm to search for the optimal shape parameters. Experiments are conducted on a 430,800 character subset of the freely-available HITPU database, a collection of 751,000 loosely-constrained handwritten Chinese characters. Results show that this new method outperforms existing representative radical approaches, including the authors' own earlier work. Improvements on the previous work are made in two aspects: automatic landmark labelling, which renders this methodology more practical, and the use of a genetic algorithm which finds the optimal shape parameters more effectively, leading to the best results so far reported on this dataset.

Recognizing online handwritten Chinese characters using RNNs with new computing architectures

Biometric authentication (BA) is becoming more and more popular. Usually, we expect that BA can make various service systems more secure, but in fact it can be more dangerous. For example, fingerprint is one of the popular biometrics for authentication. We say it is dangerous because we cannot change our fingerprints even if they are collected and duplicated by some malicious third parties. This kind of “life-long” biometrics, once they are stolen, can never be used as an authentication factor in the future. To solve the problem, we may use “changeable” biometrics. Examples include face, voice, and hand-writing characters. In this study, we use hand-writing characters. Hand-writing characters can change naturally in the aging process, they can also be changed intentionally through training. This paper investigates the feasibility of on-line user identification using hand-writing non-alphanumeric characters. Our main purpose is to develop some core technologies that can improve the security of service systems in some Asia countries that use Chinese characters.