|
| This Article | ||
| ||
| Share | ||
| Bibliographic References | ||
| Add to: | ||
 Digg  Furl  Spurl  Blink  Simpy  Google  Del.icio.us  Y!MyWeb | ||
| Search | ||
| ||
| ASCII Text | x | ||
| Cheng-Lin Liu, Masaki Nakagawa, "Precise Candidate Selection for Large Character Set Recognition by Confidence Evaluation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 6, pp. 636-642, June, 2000. | |||
| BibTex | x | ||
| @article{ 10.1109/34.862202, author = {Cheng-Lin Liu and Masaki Nakagawa}, title = {Precise Candidate Selection for Large Character Set Recognition by Confidence Evaluation}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {22}, number = {6}, issn = {0162-8828}, year = {2000}, pages = {636-642}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.862202}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - JOUR JO - IEEE Transactions on Pattern Analysis and Machine Intelligence TI - Precise Candidate Selection for Large Character Set Recognition by Confidence Evaluation IS - 6 SN - 0162-8828 SP636 EP642 EPD - 636-642 A1 - Cheng-Lin Liu, A1 - Masaki Nakagawa, PY - 2000 KW - Handwritten character recognition KW - large character set KW - candidate selection KW - confidence evaluation KW - Bayesian inference. VL - 22 JA - IEEE Transactions on Pattern Analysis and Machine Intelligence ER - | |||
Abstract—This paper proposes a precise candidate selection method for large character set recognition by confidence evaluation of distance-based classifiers. The proposed method is applicable to a wide variety of distance metrics and experiments on Euclidean distance and city block distance have achieved promising results. By confidence evaluation, the distribution of distances is analyzed to derive the probabilities of classes in two steps: output probability evaluation and input probability inference. Using the input probabilities as confidences, several selection rules have been tested and the rule that selects the classes with high confidence ratio to the first rank class produced best results. The experiments were implemented on the ETL9B database and the results show that the proposed method selects about one-fourth as many candidates with accuracy preserved compared to the conventional method that selects a fixed number of candidates.
[1] S. Mori, K. Yamamoto, and M. Yasuda, “Research on Machine Recognition of Handprinted Character,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 6, no. 4, pp. 386-405, Apr. 1984.
[2] K. Yamamoto et al., “Recognition of Handprinted Chinese Characters and Japanese Cursive Syllabury,” Proc. Seventh Int'l Conf. Pattern Recognition, pp. 385-388, 1984.
[3] M. Nakagawa, T. Oguni, and A. Homma, “A Coarse Classification of On-Line Handwritten Characters,” Proc. Fifth Int'l Workshop Frontiers Handwriting Recognition, pp. 417-420, 1996.
[4] T. Kumamoto et al., “On Speeding Candidate Selection in Handprinted Chinese Character Recognition,” Pattern Recognition, vol. 24, no. 8, pp. 793-799, 1991.
[5] C.H. Tung, H.-J. Lee, and J.-Y. Tsai, “Multi-Stage Pre-Candidate Selection in Handwritten Chinese Character Recognition Systems,” Pattern Recognition, vol. 27, no. 8, pp. 1,093-1,102, 1994.
[6] Y.Y. Tang et al., “Offline Recognition of Chinese Handwriting by Multifeature and Multilevel Classification,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 5, pp. 556-561, May 1998.
[7] Y.-H. Tseng, C.-C. Kuo, and H.-J. Lee, “Speeding up Chinese Character Recognition in an Automatic Document Reading System,” Pattern Recognition, vol. 31, no. 11, pp. 1,601-1,612, 1998.
[8] F. Sun, S. Omachi, and H. Aso, “Precise Selection of Candidates for Handwritten Character Recognition Using Feature Regions,” IEICE Trans. Information and Systems, vol. E79-D, no. 5, pp. 510-515, 1996.
[9] E. Ishidera and A. Sato, “A Reliability Estimation Method of Character Recognition on Address Reader,” Technical Report IEICE, PRMU98-160, Dec. 1998.
[10] J. Shürmann, Pattern Classification: A Unified View of Statistical and Neural Approaches. Wiley Interscience, 1996.
[11] X. Lin et al., “Adaptive Confidence Transform Based Classifier Combination for Chinese Character Recognition,” Pattern Recognition Letters, vol. 19, no. 10, pp. 975-988, 1998.
[12] D. Bouchaffra, V. Govindaraju, and S.N. Srihari, “A Methodology for Mapping Scores to Probabilities,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 21, no. 9, pp. 923-927, Sept. 1999.
[13] R.O. Duda and P.E. Hart, Pattern Classification and Scene Analysis. Wiley Interscience, 1973.
[14] L. Xu, A. Krzyzak, and C.Y. Suen, “Methods of Combining Multiple Classifiers and Their Applications to Handwriting Recognition,” IEEE Trans. Systems, Man, and Cybernetics, vol. 22, no. 3, pp. 418-435, 1992.
[15] Y.S. Huang and C.Y. Suen, A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 17, pp. 90-93, 1998.
[16] T.M. Ha, "The Optimum Class-Selective Rejection Rule," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 6, pp. 608-615, 1997.
[17] T. Horiuchi, “Class-Selective Rejection Rule to Minimize the Maximum Distance between Selected Classes,” Pattern Recognition, vol. 31, no. 10, pp. 1,579-1,588, 1998.
[18] J. Hu and M. Brown, “On-Line Handwriting Recognition with Constrained N-Best Decoding,” Proc. 13th Int'l Conf. Pattern Recognition, vol. 3, pp. 23-27, 1996.
[19] H. Yamada, K. Yamamoto, and T. Saito, “A Nonlinear Normalization Method for Handprinted Kanji Character Recognition—lLine Density Equalization,” Pattern Recognition, vol. 23, no. 9, pp. 1023-1029, 1990.
[20] C.-L. Liu, I.-J. Kim, and J.H. Kim, “High Accuracy Handwritten Chinese Character Recognition by Improved Feature Matching Method,” Proc. Fourth Int'l Conf. Document Analysis Recognition, pp. 1,033-1,037, 1997.