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An evaluation of statistical methods in handwritten hangul recognition

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Abstract

Although structural approaches have shown better performance than statistical ones in handwritten Hangul recognition (HHR), they have not been widely used in practical applications because of their vulnerability to image degradation and high computational complexity. Statistical approaches have not received high attention in HHR because their early trials were not promising enough. The past decade has seen significant improvements in statistical recognition in handwritten character recognition, including handwritten Chinese character recognition. Nevertheless, without a systematic evaluation on the effects of statistical methods in HHR, they cannot draw enough attention because of their discouraging experience. In this study, we comprehensively evaluate state-of-the-art statistical methods in HHR. Specifically, we implemented fifteen character normalization methods, five feature extraction methods, and four classification methods and evaluated their performances on two public handwritten Hangul databases. On the SERI database, statistical methods achieved the best performance of 93.71 % accuracy, which is higher than the best result achieved by structural recognizers. On the PE92 database, which has small number of samples per class, statistical methods gave slightly lower performance than the best structural recognizer.

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Abbreviations

HHR:

Handwritten hangul recognition

HCCR:

Handwritten Chinese character recognition

LN:

Linear normalization

LDE/PDE:

Line/pixel density equalization

LDPF/PDPF:

Line/pixel density projection fitting

MN:

Moment normalization

BMN:

Bi-moment normalization

CBA:

Centroid-boundary alignment

MCBA:

Modified CBA

LDPI/PDPI:

Line/pixel density projection interpolation

NBFE:

Normalization-based feature extraction

NCFE:

Normalization-cooperated feature extraction

MDC:

Minimum distance classifier

QDF:

Quadratic discrimination function

MQDF:

Modified QDF

DLQDF:

Discriminative learning QDF

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Acknowledgments

The work of Cheng-Lin Liu was supported by the National Natural Science Foundation of China (NSFC) Grants 60825301 and 60933010. The work of In-Jung Kim and Gyu-Ro Park was financially supported by the Ministry of Education, Science Technology (MEST) and National Research Foundation of Korea (NRF) through the Human Resource Training Project for Regional Innovation.

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Authors and Affiliations

  1. School of CSEE, Handong Global University, Heunghae-eup, Bukgu, Pohang, Gyeongbuk, 791-708, Republic of Korea

    Gyu-Ro Park & In-Jung Kim

  2. National Laboratory of Pattern Recognition, Institute of Automation of Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing, 100190, People’s Republic of China

    Cheng-Lin Liu

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  1. Gyu-Ro Park
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  2. In-Jung Kim
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  3. Cheng-Lin Liu
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Correspondence to In-Jung Kim.

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Park, GR., Kim, IJ. & Liu, CL. An evaluation of statistical methods in handwritten hangul recognition. IJDAR 16, 273–283 (2013). https://doi.org/10.1007/s10032-012-0191-y

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