A novel approach for structural feature extraction: Contour vs. direction

Pattern Recognition Letters

Volumn 25, Issue 9, 2004, Pages 975-988

  Verma, Brijesh ^a   Blumenstein, Michael ^b   Ghosh, Moumita ^c  

^a CENTRAL QUEENSLAND UNIVERSITY   (Australia)

^b GRIFFITH UNIVERSITY   (Australia)

^c UNIVERSITY OF BALLARAT   (Australia)

Author keywords

Feature extraction; Handwriting recognition; Neural networks; Segmentation

Indexed keywords

BOUNDARY CONDITIONS; CONTOUR FOLLOWERS; EVALUATION;

SEGMENTED CURSIVE HANDWRITING; STRUCTURAL FEATURES;

FEATURE EXTRACTION;

ARTIFICIAL NEURAL NETWORK; PATTERN RECOGNITION;

EID: 2942528815     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2004.02.013     Document Type: Article

References (29)

1. Blumenstein, M., Verma, B., 1999a. A new segmentation algorithm for handwritten word recognition. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN'99), Washington, DC. pp. 2893-2898
2. Blumenstein, M., Verma, B., 1999b. Neural solutions for the segmentation and recognition of difficult words from a benchmark database. In: Proceedings of the 5th International Conference on Document Analysis and Recognition (ICDAR'99), Bangalore, India. pp. 281-284
3. Blumenstein, M., Verma, B.K., 2001. Analysis of segmentation performance on the CEDAR benchmark database. In: Proceedings of the Sixth International Conference on Document Analysis and Recognition (ICDAR'01), Seattle, USA. pp. 1142-1146
4. Blumenstein, M., Verma, B.K., Basli, H., 2003. A novel feature extraction technique for the recognition of segmented handwritten characters. In: 7th International Conference on Document Analysis and Recognition (ICDAR'03), Edinburgh, Scotland, pp. 137-141
5. Bozinovic R.M. Srihari S.N. Off-line cursive script word recognition IEEE Trans. Pattern Anal. Machine Intell. 11 1989 68-83
6. Bruel, T., 1994. Design and implementation of a system for recognition of handwritten responses on US census forms. In: Proceedings of the IAPR Workshop on Document Analysis Systems, Kaiserlautern, Germany. pp. 237-264
7. Burges, C.J.C., Be, J.I., Nohl, C.R., 1992. Recognition of handwritten cursive postal words using neural networks. In: Proceedings of the 5th USPS Advanced Technology Conference. pp. 117-124
8. Cai J. Liu Z.-Q. Integration of structural and statistical information for unconstrained handwritten numeral recognition IEEE Trans. Pattern Anal. Machine Intell. 21 1999 263-270
9. Camastra F. Vinciarelli A. Cursive character recognition by learning vector quantization Pattern Recog. Lett. 22 2001 625-629
10. Camastra F. Vinciarelli A. Combining neural gas and learning vector quantization for cursive character recognition Neurocomputing 51 2003 147-159
11. Cho S.-B. Neural-network classifiers for recognizing totally unconstrained handwritten numerals IEEE Trans. Neural Networks 8 1997 43-53
12. Frishkopf L.S. Harmon L.D. Machine reading of cursive script Cherry C. Information Theory 1961 300-316 Butterworth London
13. Gader, P.D., Magdi, M., Chiang, J.-H., 1992. Segmentation-based handwritten word recognition. In: Proceedings of the 5th USPS Advanced Technology Conference. pp. 215-226
14. Gader P.D. Mohamed M. Chiang J.-H. Handwritten word recognition with character and inter-character neural networks IEEE Trans. Systems, Man, Cybernetics--Part B: Cybernetics 27 1997 158-164
15. Guillevic, D., Suen, C.Y., 1998. HMM-KNN word recognition engine for bank cheque processing. In: Proceedings of the 14th International Conference on Pattern Recognition, Brisbane, Australia. pp. 1526-1529
16. Hanmandlu M. Murali Mohan K.R. Chakraborty S. Goyal S. Roy Choudhury D. Unconstrained handwritten character recognition based on fuzzy logic Pattern Recog. 36 2003 603-623
17. Hull J.J. A database for handwritten text recognition IEEE Trans. Pattern Anal. Machine Intell. 16 1994 550-554
18. Kim G. Govindaraju V. A lexicon driven approach to handwritten word recognition for real-time applications IEEE Trans. Pattern Anal. Machine Intell. 19 1997 366-379
19. Kimura, F., Tsuruoka, S., Shridhar, M., Chen, Z., 1992. Context-directed handwritten word recognition for postal service applications. In: Proceedings of the 5th USPS Advanced Technology Conference. pp. 199-213
20. Kimura F. Tsuruoka S. Miyake Y. Shridhar M. A lexicon directed algorithm for recognition of unconstrained handwritten words IEICE Trans. Inform. Syst. E77-D 1994 785-793
21. Kimura, F., Kayahara, N., Miyake, Y., Shridhar, M., 1997. Machine and human recognition of segmented characters from handwritten words. In: 4th International Conference on Document Analysis and Recognition (ICDAR'97), Ulm, Germany. pp. 866-869
22. Lazzerini B. Marcelloni F. A Linguistic fuzzy recogniser of off-line handwritten characters Pattern Recog. Lett. 21 2000 319-327
23. Lee S.-W. Off-line recognition of totally unconstrained handwritten numerals using multilayer cluster neural network IEEE Trans. Pattern Anal. Machine Intell. 18 1996 648-652
24. Otsu N. A threshold selection method from gray-level histograms IEEE Trans. Systems, Man, Cybernetics SMC-9 1979 62-66
25. Paquet, T., Lecourtier, Y., 1991. Handwriting recognition: Application on bank cheques. In: Proceedings of the First International Conference on Document Analysis and Recognition, St. Malo, France. pp. 749-757
26. Plamondon R. Srihari S.N. On-line and off-line handwriting recognition: A comprehensive survey IEEE Trans. Pattern Anal. Machine Intell. 22 2000 63-84
27. Singh, S., Hewitt, M., 2000. Cursive digit and character recognition on cedar database. In: International Conference on Pattern Recognition (ICPR 2000), Barcelona, Spain. pp. 569-572
28. Suen C.Y. Legault R. Nadal C. Cheriet M. Lam L. Building a new generation of handwriting recognition systems Pattern Recog. Lett. 14 1993 305-315
29. Yamada H. Nakano Y. Cursive handwritten word recognition using multiple segmentation determined by contour analysis IEICE Trans. Inform. Syst. E79-D 1996 464-470