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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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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DOI: https://doi.org/10.1007/s10032-012-0191-y