Segmentation of Arabic Handwritten Numeral Strings Based on Watershed Approach

Authors: Nidal F. Shilbayeh, Remah W. Al-Khatib, Sameer A. Nooh

Abstract:

Arabic offline handwriting recognition systems are considered as one of the most challenging topics. Arabic Handwritten Numeral Strings are used to automate systems that deal with numbers such as postal code, banking account numbers and numbers on car plates. Segmentation of connected numerals is the main bottleneck in the handwritten numeral recognition system.  This is in turn can increase the speed and efficiency of the recognition system. In this paper, we proposed algorithms for automatic segmentation and feature extraction of Arabic handwritten numeral strings based on Watershed approach. The algorithms have been designed and implemented to achieve the main goal of segmenting and extracting the string of numeral digits written by hand especially in a courtesy amount of bank checks. The segmentation algorithm partitions the string into multiple regions that can be associated with the properties of one or more criteria. The numeral extraction algorithm extracts the numeral string digits into separated individual digit. Both algorithms for segmentation and feature extraction have been tested successfully and efficiently for all types of numerals.

Keywords: Handwritten numerals, segmentation, courtesy amount, feature extraction, numeral recognition.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.3462059

References:

[1] Shilbayeh, N. F., Aqel, M. M., & Alkhateeb, R. (2013). Recognition Offline Handwritten Hindi Digits Using Multilayer Perceptron Neural Networks, Recent Advances in Computer Science and Networking, Proceedings of the 2nd International Conference on Information Technology and Computer Networks (ITCN '13) Antalya, Turkey October 8-10.
[2] Shilbayeh, N. F., Alwakeel, M. M., & Naser, M. M. (2013). An efficient neural network for recognizing gestural Hindi digits. American Journal of Applied Sciences, 10(9), 938-951.
[3] Shilbayeh, N.F. and Iskandarani, M.Z., 2008. Effect of Hidden Layer Neurons on the Classification of Optical Character Recognition Typed Arabic Numerals 1.
[4] Shilbayeh, N., Raho, G. and Alkateeb, M., 2009. An efficient Structural Mouse Gesture approach for recognizing Hindi digits. Applied Sci, 9, pp.3469-3479.
[5] Wshah, S.R. and Campanelli, M.R., Xerox Corp, 2015. Character recognition method and system using digit segmentation and recombination. U.S. Patent Application 14/312,177.
[6] Ribas, F. C., Oliveira, L. S., Britto Jr, A. S., & Sabourin, R. (2013). Handwritten digit segmentation: a comparative study. International Journal on Document Analysis and Recognition (IJDAR), 16(2), 127-137.
[7] Parisi, R., Di Claudio, E.D., Lucarelli, G. and Orlandi, G., 1998, June. Car plate recognition by neural networks and image processing. In Circuits and Systems, 1998. ISCAS'98. Proceedings of the 1998 IEEE International Symposium on (Vol. 3, pp. 195-198). IEEE.
[8] Palacios, R., Gupta, A. and Wang, P.S., 2004. Handwritten bank check recognition of courtesy amounts. International Journal of Image and Graphics, 4(02), pp.203-222.
[9] Khan, S.A., 1998. Character segmentation heuristics for check amount verification (Doctoral dissertation, Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science).
[10] Al_barraq, M.O. and Mehrotra, S.C., 2015. Recognition of Arabic Handwritten Amount in Cheque through Windowing Approach. International Journal of Computer Applications, 115(10).
[11] Sivanandam, S.N. and Deepa, S.N., 2006. Introduction to neural networks using Matlab 6.0. Tata McGraw-Hill Education.