Invariant Primitives for Handwritten Arabic Script: A Contrastive Study of Four Feature Sets

The choice of relevant features is very decisive in handwriting recognition rate. Our aim is to present some useful structural and statistical features and see their degree of variability. In this paper, we start with a description of the variability of the Arabic handwriting and the way how to reduce it. Four kinds of feature sets used by our handwriting systems are then presented evaluated and discussed. The comparison is carried on a database of images from IFN/ENIT databases. The Neural Network Multilayer perceptrons is our method of classification. A contrastive study of these primitives is done according to recognition their time and memory consuming and their variability degree.

[1]  Sriganesh Madhvanath,et al.  Pruning large lexicons using generalized word shape descriptors , 1997, Proceedings of the Fourth International Conference on Document Analysis and Recognition.

[2]  D. P. Mital,et al.  Texture segmentation using Gabor filters , 2000, KES'2000. Fourth International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies. Proceedings (Cat. No.00TH8516).

[3]  Venu Govindaraju,et al.  Contour-based image preprocessing for holistic handwritten word recognition , 1997, Proceedings of the Fourth International Conference on Document Analysis and Recognition.

[4]  K. Yamada,et al.  On-line Japanese character recognition experiments by an off-line method based on normalization-cooperated feature extraction , 1993, Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR '93).

[5]  R. Sabourin,et al.  Synthetic parameters for handwriting classification , 1997, Proceedings of the Fourth International Conference on Document Analysis and Recognition.

[6]  Jamshid Shanbehzadeh,et al.  Persian/arabic handwritten word recognition using M-band packet wavelet transform , 2008, Image Vis. Comput..

[7]  Charles R. Giardina,et al.  Elliptic Fourier features of a closed contour , 1982, Comput. Graph. Image Process..

[8]  Adnan Amin.  Recognition of hand-printed characters based on structural description and inductive logic programming , 2003, Pattern Recognit. Lett..

[9]  Ching Y. Suen,et al.  Structural classification and relaxation matching of totally unconstrained handwritten zip-code numbers , 1988, Pattern Recognit..

[10]  Tieniu Tan,et al.  Personal identification based on handwriting , 2000, Pattern Recognit..

[11]  Atul K. Chhabra,et al.  High-order statistically derived combinations of geometric features for handprinted character recognition , 1993, Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR '93).

[12]  Chafic Mokbel,et al.  Arabic handwriting recognition using baseline dependant features and hidden Markov modeling , 2005, Eighth International Conference on Document Analysis and Recognition (ICDAR'05).

[13]  Hirobumi Nishida.  A structural model of continuous transformation of handwritten characters , 1995, Proceedings of 3rd International Conference on Document Analysis and Recognition.

[14]  Seong-Whan Lee,et al.  Nonlinear shape normalization methods for gray-scale handwritten character recognition , 1997, Proceedings of the Fourth International Conference on Document Analysis and Recognition.

[15]  Mokhtar Sellami,et al.  Classifiers combination and syntax analysis for Arabic literal amount recognition , 2006, Eng. Appl. Artif. Intell..

[16]  Volker Märgner,et al.  Comparison of Different Preprocessing and Feature Extraction Methods for Offline Recognition of Handwritten ArabicWords , 2007, ICDAR.

[17]  Abdel Belaïd,et al.  Local Normalization Towards Global Recognition of Arabic Handwritten Script , 2001 .

[18]  Ching Y. Suen,et al.  Automatic reading of cursive scripts using human knowledge , 1997, Proceedings of the Fourth International Conference on Document Analysis and Recognition.

[19]  Mohammad S. Khorsheed,et al.  Recognising handwritten Arabic manuscripts using a single hidden Markov model , 2003, Pattern Recognit. Lett..

[20]  M. Pechwitz,et al.  IFN/ENIT: database of handwritten arabic words , 2002 .