Recognition of handwritten Chinese characters by critical region analysis

Leung, Ka-Chung; Leung, C. H.

doi:10.1016/j.patcog.2009.09.001

Cited by 43 publications

(16 citation statements)

References 15 publications

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“…Some methods firstly find the critical different regions of the similar character pair and then extract features within the critical region. Reference [10] uses LDA to get the projection feature and projects the feature back to find the critical region. In [11] the author employs average symmetric uncertainty method to detect critical region.…”

Section: Related Workmentioning

confidence: 99%

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Similar Pattern Discriminant Analysis for Improving Chinese Character Recognition Accuracy

Wang

Liu

Ding

2013

2013 12th International Conference on Document Analysis and Recognition

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In this paper, a similar pattern discriminant analysis method is proposed. It optimizes the feature projection matrix based on similar pattern pairs and aims to extract targeted features for similar pattern discrimination. For improving Chinese character recognition accuracy, we introduce a cascade modified quadratic discriminant function (MQDF) model to combine linear discriminant analysis (LDA) and similar pattern discriminant analysis. The proposed method is investigated and compared with compound Mahalanobis function (CMF) on two data sets. The results indicate that the cascade MQDF achieves a better improvement and higher recognition accuracies than CMF. The relative recognition errors have been decreased up to 19.73% and 15.59% respectively on HCL2000 and THU-HCD datasets with respect to single MQDF.

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Section: Related Workmentioning

confidence: 99%

“…However the critical region location itself sometimes is a difficult problem for computer. Reference [11] also reported that the critical region detection consumes more than 340 ms and 5600 ms in [11] and [10] respectively. It seems that the computational complexity of critical region detection is little high for real application.…”

Section: Related Workmentioning

confidence: 99%

Similar Pattern Discriminant Analysis for Improving Chinese Character Recognition Accuracy

Wang

Liu

Ding

2013

2013 12th International Conference on Document Analysis and Recognition

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“…During the last few years the focus in handwritten character recognition has shifted from Arabic digits [1], Chinese [2] and Kanji handwritten character recognition toward scripts like Farsi [3], Devnagari, Telegu, Oriya, Bengali [4], [5] etc.…”

Section: Motivationmentioning

confidence: 99%

A Semi-supervised Ensemble Learning Approach for Character Labeling with Minimal Human Effort

Vajda

Junaidi²,

Fink³

2011

2011 International Conference on Document Analysis and Recognition

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One of the major issues in handwritten character recognition is the efficient creation of ground truth to train and test the different recognizers. The manual labeling of the data by a human expert is a tedious and costly procedure. In this paper we propose an efficient and low-cost semiautomatic labeling system for character datasets. First, the data is represented in different abstraction levels, which is clustered after in an unsupervised manner. The different clusters are labeled by the human experts and finally an unanimity voting is considered to decide if a label is accepted or not. The experimental results prove that labeling only less than 0.5% of the training data is sufficient to achieve 86.21% recognition rate for a brand new script (Lampung) and 94.81% for the MNIST benchmark dataset, considering only a K-nearest neighbor classifier for recognition.

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“…Additional features are extracted from these critical regions and used to train the confusion pair's classifier. Xu et al [29] proposed an average symmetric uncertainty (ASU)-based critical region selection method, and they showed that the critical regions selected by their method contain more discriminative information than by the method proposed in [28]. Shao et al [30] proposed a self-adaptive critical region-based method for confusion pair discrimination.…”

Section: Introductionmentioning

confidence: 99%

A character image restoration method for unconstrained handwritten Chinese character recognition

Shao

Wang

Xiao

2014

IJDAR

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Despite the success of methods on constrained handwriting databases, recognition of unconstrained handwritten Chinese characters remains a big challenge. One difficulty for recognizing unconstrained handwritting is that some connected strokes are involved or some strokes are omitted. In this paper, a character image restoration method is proposed for unconstrained handwritten Chinese character recognition. In this method, the observed character image is modeled as the combination of the ideal character image with two types of noise images: the omitted stroke noise image and the added stroke noise image. To preserve the original gradient features, restoration is done on the gradient features. The estimated features are then used to discriminate similar characters. To show the effectiveness of the proposed method, we extend some state-of-the-art classifiers based on the estimated features. Experimental results show that the extended classifiers outperform the original state-of-the-art classifiers. This demonstrates that the estimated features are useful for further improving the recognition rate.

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Recognition of handwritten Chinese characters by critical region analysis

Cited by 43 publications

References 15 publications

Similar Pattern Discriminant Analysis for Improving Chinese Character Recognition Accuracy

Similar Pattern Discriminant Analysis for Improving Chinese Character Recognition Accuracy

A Semi-supervised Ensemble Learning Approach for Character Labeling with Minimal Human Effort

A character image restoration method for unconstrained handwritten Chinese character recognition

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