Off-Line, Handwritten Numeral Recognition by Perturbation Method

This paper presents a new approach to off-line, handwritten numeral recognition. From the concept of perturbation due to writing habits and instruments, we propose a recognition method which is able to account for a variety of distortions due to eccentric handwriting. We tested our method on two worldwide standard databases of isolated numerals, namely CEDAR and NIST, and obtained 99.09 percent and 99.54 percent correct recognition rates at no-rejection level respectively. The latter result was obtained by testing on more than 170000 numerals.

[1]  Karl Sims,et al.  Handwritten Character Classification Using Nearest Neighbor in Large Databases , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Henry S. Baird,et al.  Document image defect models , 1995 .

[3]  Julian R. Ullmann,et al.  Pattern recognition techniques , 1973 .

[4]  Toru Wakahara,et al.  Shape Matching Using LAT and its Application to Handwritten Numeral Recognition , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Kazuhiko Yamamoto,et al.  Structured Document Image Analysis , 1992, Springer Berlin Heidelberg.

[6]  Sahibsingh A. Dudani.  The Distance-Weighted k-Nearest-Neighbor Rule , 1976, IEEE Transactions on Systems, Man, and Cybernetics.

[7]  C. K. Chow,et al.  An optimum character recognition system using decision functions , 1957, IRE Trans. Electron. Comput..

[8]  Patrick J. Grother,et al.  The First Census Optical Character Recognition Systems Conference | NIST , 1992 .

[9]  Ching Y. Suen,et al.  Computer recognition of unconstrained handwritten numerals , 1992, Proc. IEEE.

[10]  Eberhard Mandler,et al.  Document analysis-from pixels to contents , 1992 .

[11]  Harris Drucker,et al.  Boosting Performance in Neural Networks , 1993, Int. J. Pattern Recognit. Artif. Intell..

[12]  Jonathan J. Hull,et al.  A Database for Handwritten Text Recognition Research , 1994, IEEE Trans. Pattern Anal. Mach. Intell..