High accuracy handwritten Chinese character recognition using LDA-based compound distances

Gao, Tianfu; Liu, Cheng‐Lin

doi:10.1016/j.patcog.2008.04.011

Cited by 54 publications

(38 citation statements)

References 11 publications

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“…We take it as a baseline classifier and use compound distance functions to further improve the accuracy. To further improve the recognition rate, CMF-and LDA-based compound distance in the original space [2] are adopted to discriminate similar character pairs.…”

Section: Classificationmentioning

confidence: 99%

“…A lot of methods have been proposed, and very high recognition rates have been obtained on most of databases [1][2][3][4][5][6][7][8][9][10][11][12]. Generally, a HCCR system includes character image normalization, feature extraction and classification.…”

Section: Introductionmentioning

confidence: 99%

“…The compound Mahalanobis function (CMF) method, proposed by Suzuki et al [26], combines the pair discrimination measures with the class-wise Mahalanobis distance. Gao and Liu [2,27] proposed a LDA-based compound distance to discriminate similar character pairs. They showed that under restrictive assumptions, the previous CMF is a special case of the LDA-based compound distance method.…”

Section: Introductionmentioning

confidence: 99%

“…4.1, and the resulted compound MQDF (CMQDF) method outperforms MQDF and CMF methods. We also extend two typical confusion pair discrimination methods (LDA [2] and SVM [35]) in Sect. 4.2, and experimental results show that the extended methods (RLDA and RSVM) outperform the original methods.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A character image restoration method for unconstrained handwritten Chinese character recognition

Shao

Wang

Xiao

2014

IJDAR

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“…The number of classes is defined as the number of different characters, whereas a sample is defined as the character reproduced by different writers for each class (or character). The HCL2000 database was used by Long and Jin (2008) as well as Liu and Ding (2005), the ETL9B database by Dong et al (2005) as well as Gao and Liu (2008), and the CASIA database by Gao and Liu (2008), respectively. The existing databases (Table 1) store many samples of different writing styles for each character, in order to cope with the problem of handwriting variation of different writers.…”

Section: Databasementioning

confidence: 99%

Efficient online handwritten Chinese character recognition system using a two-dimensional functional relationship model

Fah¹,

Lee²,

Rijal³

et al. 2010

International Journal of Applied Mathematics and Computer Science

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This paper presents novel feature extraction and classification methods for online handwritten Chinese character recognition (HCCR). The X-graph and Y -graph transformation is proposed for deriving a feature, which shows useful properties such as invariance to different writing styles. Central to the proposed method is the idea of capturing the geometrical and topological information from the trajectory of the handwritten character using the X-graph and the Y-graph. For feature size reduction, the Haar wavelet transformation was applied on the graphs. For classification, the coefficient of determination (R 2 p ) from the two-dimensional unreplicated linear functional relationship model is proposed as a similarity measure. The proposed methods show strong discrimination power when handling problems related to size, position and slant variation, stroke shape deformation, close resemblance of characters, and non-normalization. The proposed recognition system is applied to a database with 3000 frequently used Chinese characters, yielding a high recognition rate of 97.4% with reduced processing time of 75.31%, 73.05%, 58.27% and 40.69% when compared with recognition systems using the city block distance with deviation (CBDD), the minimum distance (MD), the compound Mahalanobis function (CMF) and the modified quadratic discriminant function (MQDF), respectively. High precision rates were also achieved.

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Class Discriminatory Information for Unbound Granular Layers Using Statistical Pattern Recognition Techniques

Ashtiani¹

2021

Lecture Notes in Civil Engineering

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High accuracy handwritten Chinese character recognition using LDA-based compound distances

Cited by 54 publications

References 11 publications

A character image restoration method for unconstrained handwritten Chinese character recognition

A character image restoration method for unconstrained handwritten Chinese character recognition

Efficient online handwritten Chinese character recognition system using a two-dimensional functional relationship model

Class Discriminatory Information for Unbound Granular Layers Using Statistical Pattern Recognition Techniques

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