Line segment distribution of sketches for Persian signature recognition

A novel fast method for line segment extraction based on chain code representation of thinned sketches (or edge maps) is presented and exploited for Persian signature recognition. The method has a parallel nature and can be employed on parallel machines. It breaks the macro chains into several micro chains after applying shifting, smoothing and differentiating. The micro chains are then approximated by straight line segments. Length and position distributions of the extracted line segments are used to make a compact feature vector for Iranian cursive signature. The feature vector is invariant under affine transforms and can be used effectively in paperless office projects. Experimental results show fast response and accurate recognition/retrieval rate.

[1]  A. Dehghani,et al.  Off-line recognition of isolated Persian handwritten characters using multiple hidden Markov models , 2001, Proceedings International Conference on Information Technology: Coding and Computing.

[2]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Yongsheng Gao,et al.  Face Recognition Using Line Edge Map , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  J. B. Burns,et al.  Extracting straight lines , 1987 .

[5]  Abdolah Chalechale,et al.  An Abstract Image Representation Based on Edge Pixel Neighborhood Information (EPNI) , 2002, EurAsia-ICT.

[6]  Mahmoud Reza Hashemi,et al.  Persian cursive script recognition , 1995, Proceedings of 3rd International Conference on Document Analysis and Recognition.

[7]  Ki-Sang Hong,et al.  Fast line segment grouping method for finding globally more favorable line segments , 2002, Pattern Recognit..

[8]  Karim Faez,et al.  Off-line unconstrained Farsi handwritten word recognition using fuzzy vector quantization and hidden Markov word models , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[9]  Thomas S. Huang,et al.  Image processing , 1971 .

[10]  A. H. Etemadi.  Robust segmentation of edge data , 1992 .

[11]  Jianlong Zhu,et al.  Vehicle license image segmentation using wavelet transform , 2001, Proceedings of 2001 International Symposium on Intelligent Multimedia, Video and Speech Processing. ISIMP 2001 (IEEE Cat. No.01EX489).

[12]  Alberto Del Bimbo,et al.  Visual information retrieval , 1999 .

[13]  Roland T. Chin,et al.  On Image Analysis by the Methods of Moments , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  M. Dehghan,et al.  Farsi handwritten character recognition with moment invariants , 1997, Proceedings of 13th International Conference on Digital Signal Processing.

[15]  Karim Faez,et al.  Feature extraction with wavelet transform for recognition of isolated handwritten Farsi/Arabic characters and numerals , 2002, 2002 14th International Conference on Digital Signal Processing Proceedings. DSP 2002 (Cat. No.02TH8628).

[16]  B. S. Manjunath,et al.  A contour-based approach to multisensor image registration , 1995, IEEE Trans. Image Process..

[17]  Mohammad Bagher Menhaj,et al.  Simultaneous segmentation and recognition of Farsi/Latin printed texts with MLP , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

[18]  Randal C. Nelson,et al.  Finding Line Segments by Stick Growing , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Ari Visa,et al.  Shape recognition of irregular objects , 1996, Other Conferences.

[20]  Flávio Bortolozzi,et al.  The interpersonal and intrapersonal variability influences on off-line signature verification using HMM , 2002, Proceedings. XV Brazilian Symposium on Computer Graphics and Image Processing.

[21]  Alberto Del Bimbo,et al.  Taking into Consideration Sports Semantic Annotation of Sports Videos Content-based Multimedia Indexing and Retrieval , 2002 .