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

The paper presents a novel approach for extracting structural features from segmented cursive handwriting. The proposed approach is based on the contour code and stroke direction. The contour code feature utilises the rate of change of slope along the contour profile in addition to other properties such as the ascender and descender count, start point and end point. The direction feature identifies individual line segments or strokes from the character's outer boundary or thinned representation and highlights each character's pertinent direction information. Each feature is investigated employing a benchmark database and the experimental results using the proposed contour code based structural feature are very promising. A comparative evaluation with the directional feature and existing transition feature is included.

[1]  Madasu Hanmandlu,et al.  Unconstrained handwritten character recognition based on fuzzy logic , 2003, Pattern Recognit..

[2]  Francesco Camastra,et al.  Cursive character recognition by learning vector quantization , 2001, Pattern Recognit. Lett..

[3]  D. Guillevic,et al.  HMM-KNN word recognition engine for bank cheque processing , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[4]  Malayappan Shridhar,et al.  High accuracy character recognition algorithm using fourier and topological descriptors , 1984, Pattern Recognit..

[5]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[6]  Robert B. Ash,et al.  Information Theory , 2020, The SAGE International Encyclopedia of Mass Media and Society.

[7]  Gyeonghwan Kim,et al.  A Lexicon Driven Approach to Handwritten Word Recognition for Real-Time Applications , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Thomas M. Breuel.  Design and Implementation of a System for the Recognition of Handwritten Responses on US Census Forms , 1994 .

[9]  Brijesh Verma,et al.  Neural-based solutions for the segmentation and recognition of difficult handwritten words from a benchmark database , 1999, Proceedings of the Fifth International Conference on Document Analysis and Recognition. ICDAR '99 (Cat. No.PR00318).

[10]  Jung-Hsien Chiang,et al.  Handwritten word recognition with character and inter-character neural networks , 1997, IEEE Trans. Syst. Man Cybern. Part B.

[11]  Fumitaka Kimura,et al.  Improvements of a lexicon directed algorithm for recognition of unconstrained handwritten words , 1993, Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR '93).

[12]  Jinhai Cai,et al.  Integration of structural and statistical information for unconstrained handwritten numeral recognition , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[13]  Francesco Camastra,et al.  Combining neural gas and learning vector quantization for cursive character recognition , 2003, Neurocomputing.

[14]  Hiroshi Yamada,et al.  Cursive handwritten word recognition using multiple segmentation determined by contour analysis , 1996 .

[15]  Ching Y. Suen,et al.  Building a new generation of handwriting recognition systems , 1993, Pattern Recognit. Lett..

[16]  Brijesh Verma,et al.  Analysis of segmentation performance on the CEDAR benchmark database , 2001, Proceedings of Sixth International Conference on Document Analysis and Recognition.

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

[18]  LeeSeong-Whan.  Off-Line Recognition of Totally Unconstrained Handwritten Numerals Using Multilayer Cluster Neural Network , 1996 .

[19]  Sung-Bae Cho,et al.  Neural-network classifiers for recognizing totally unconstrained handwritten numerals , 1997, IEEE Trans. Neural Networks.

[20]  Sameer Singh,et al.  Cursive digit and character recognition in CEDAR database , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[21]  Beatrice Lazzerini,et al.  A linguistic fuzzy recogniser of off-line handwritten characters , 2000, Pattern Recognit. Lett..

[22]  Brijesh Verma,et al.  A new segmentation algorithm for handwritten word recognition , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

[23]  Malayappan Shridhar,et al.  A Lexicon Directed Algorithm for Recognition of Unconstrained Handwritten Words (Special Issue on Document Analysis and Recognition) , 1994 .

[24]  Y. Miyake,et al.  Machine and human recognition of segmented characters from handwritten words , 1997, Proceedings of the Fourth International Conference on Document Analysis and Recognition.

[25]  Brijesh Verma,et al.  A novel feature extraction technique for the recognition of segmented handwritten characters , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[26]  Young-Joon Kim,et al.  Off-line recognition of totally unconstrained handwritten numerals using multilayer cluster neural network , 1994, Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 3 - Conference C: Signal Processing (Cat. No.94CH3440-5).

[27]  Sargur N. Srihari,et al.  On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[28]  Sargur N. Srihari,et al.  Off-Line Cursive Script Word Recognition , 1989, IEEE Trans. Pattern Anal. Mach. Intell..