An improved handwritten Chinese character recognition system using support vector machine

Dong, Jianxiong; Krzyak, Adam; Suen, Ching Y.

doi:10.1016/j.patrec.2005.03.006

Cited by 83 publications

(36 citation statements)

References 13 publications

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“…A strategy to alleviate the computation cost is to use a statistical or neural classifier for selecting two candidate classes, which are then discriminated by SVM [13]. Dong et al used a one-against-others scheme for large set Chinese character recognition with fast training [14]. The SVM classifier with RBF kernel mostly gives the highest accuracy.…”

Section: B Kernel Methods-svmmentioning

confidence: 99%

Performance Comparison of SVM and K-NN for Oriya Character Recognition

Mohanty¹,

Bebartta²

2011

SpecialIssue

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Abstract-Image classification is one of the most important branch of Artificial intelligence; its application seems to be in a promising direction in the development of character recognition in Optical Character Recognition (OCR). Character recognition (CR) has been extensively studied in the last half century and progressed to the level, sufficient to produce technology driven applications. ow the rapidly growing computational power enables the implementation of the present CR methodologies and also creates an increasing demand on many emerging application domains, which require more advanced methodologies. Researchers for the recognition of Indic Languages and scripts are comparatively less with other languages. There are lots of different machine learning algorithms used for image classification nowadays. In this paper, we discuss the characteristics of some classification methods such as Support Vector Machines (SVM) and K-earest eighborhood (K-) that have been applied to Oriya characters. We will discuss the performance of each algorithm for character classification based on drawing their learning curve, selecting parameters and comparing their correct rate on different categories of Oriya characters. It has been observed that Support Vector Machines outperforms among both the classifiers.

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Section: B Kernel Methods-svmmentioning

confidence: 99%

Performance Comparison of SVM and K-NN for Oriya Character Recognition

Mohanty¹,

Bebartta²

2011

SpecialIssue

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“…Recognition rate of SVM classifier is found to be highest among the literature considered for cursive character recognition. Jiang-Yiog Dong et al [13]. have presented a technique to improve nonlinear normalization scheme of Chinese character recognition which was earlier propose by Yamada et al(1990) [14].…”

Section: Svm For Off-line Handwritten Character Recognitionmentioning

confidence: 99%

Off-Line Handwritten Character Recognition System Using Support Vector Machine

Katiyar¹

2017

AJNNA

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Selection of classifiers and feature extraction methods has a prime role in achieving best possible classification accuracy in character recognition system. Issues of character recognition system related to choice of classifiers and feature extraction methods can be resolved through these objectives. In this proposed work an efficient Support Vector Machine based off-line handwritten character recognition system has been developed. The experiments have been performed using well known standard database acquired from CEDAR, also seven different approaches of feature extraction techniques have been proposed to construct the final feature vector. It is evident from the experimental results that the performance of Support Vector Machine outperforms other state of art techniques reported in literature.

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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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An improved handwritten Chinese character recognition system using support vector machine

Cited by 83 publications

References 13 publications

Performance Comparison of SVM and K-NN for Oriya Character Recognition

Performance Comparison of SVM and K-NN for Oriya Character Recognition

Off-Line Handwritten Character Recognition System Using Support Vector Machine

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

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