Handwritten Chinese character analysis and preclassification using stroke structural sequence

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.

Today, more and more foreigners go to China and are emerged into studying Chinese. Thereinto, how to write Chinese characters is a very important and difficult task. As computers and internets develop, many teachers for Chinese Education want to use pattern recognition technologies to automatically evaluate and direct the quality of Chinese characters written by foreign students through document scanning. Actually, this is a handwritten character evaluation and identification problem. In this paper, we investigate and compare several character identification methods for Chinese Education within a classification framework. First, some two-class classification techniques with different features and classifiers (BP neural networks and SVMs) are investigated to identify each handwritten Chinese character. Moreover, in character identification, positive examples are always conjunctive, but negative examples are diffused in most cases. Consequently, we use one-class classification technique (one-class SVMs) to perform this handwritten character identification. In order to overcome the sensitivity to the SVM parameters, we propose a variant one-class SVM system - Bagged One-Class SVMs, which integrate many one-class SVMs with sample bagging. Some experiments of evaluating real handwritten Chinese characters by foreigners are performed, which show that general handwritten character identification is a big challenge and one-class classification technique is a potential researching and developing direction.

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.

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.

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.

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.

Multi-stroke relaxation matching method for handwritten chinese character recognition

Feature extraction is the key part of handwritten Chinese character recognition.It is found that the directional feature dug by elastic mesh is suitable for Chinese character recognition.In view of the usual vertical-horizontal elastic mesh's disadvantages in left falling stroke and right falling stroke,we presented a new elastic mesh structuring method-diagonal elastic mesh,which was composed of diagonal beelines of 45° and 135°,and divided the Chinese character image into many diamonds.This paper took the two kinds of mesh and their combinations into the handwritten recognition experiment.The experimental results have verified the diagonal elastic mesh's validity and that the double elastic mesh has very high recognition rate.

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.

An efficient method of Chinese handwritten address character string segmentation and recognition is presented. First, an address string image is presegmented into several radicals using stroke extraction and stroke mergence. Next, the radical series obtained by presegmentation merge into different character image series according to different merging paths. After that, the optimal merging path is selected using recognition and semantic information. The recognition information is given by the character classifier. The semantic information is obtained from large scale address database containing more than one hundred thousand address items. Finally, the optimal recognition results of the character image series which are combined by radical series according to the optimal merging paths are obtained. In experiments on 897 mail images, the proposed method achieves correct rate of 85 percent while the error rate is 15 percent.

Hierarchical attributed graph representation and recognition of handwritten chinese characters

In this paper, we propose a novel method for recognizing occluded offline handwritten Chinese characters based on deep convolutional generative adversarial network (DCGAN) and improved GoogLeNet. Different from previous methods, our proposed method is capable of inpainting and recognizing occluded characters without needing to know the concrete positions of corrupted regions. First, the generator and discriminator of DCGAN are combined to generate realistic Chinese characters from corrupted images, and the contextual loss and the content loss are further used to inpaint generated images. Finally, we use the improved GoogLeNet with traditional feature extraction methods to recognize the recovered handwritten Chinese characters. The proposed method is evaluated on the extended CASIA-HWDB1.1 dataset for two challenging inpainting tasks with different portions of blocks or random missing pixels. Experimental results show that our method can achieve higher repair rates and higher recognition accuracies than most of existing methods.

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%.