Abstract
This paper presents a holistic recognition of handwritten word based on prototype recognition. Its main objective is to arrive at a reduced number of candidates corresponding to a given prototype class and to determine from them the handwritten class to be recognized. The proposed work involves only an accurate extraction and representation of three zones namely; lower, upper and central zones from the off-line cursive word to obtain a descriptor which provides a coarse characterization of word shape. The recognition system is based primarily on the sequential combination of Hopfield model and MLP based classifier for prototype recognition yielding the handwritten recognition. The handwritten words representing the 27 amount classes are clustered in 16 prototypes or models. These prototypes are used as fundamental memories by the Hopfield network that is subsequently fed to MLP for classification. Experimental results carried out on real images of isolated wholly lower case legal amount bank checks written in mixed cursive and discrete style are presented showing an achievement of 86.5 and 80.75 % rate for prototype and handwritten word recognition respectively. They confirm that the proposed approach shows promising performance results and can be successfully used in processing of poor quality bank checks.
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References
Dimauro, G., Impedovo, S., Pirlo, G., Salzo, A.: Automatic bank check processing a new engineered system, vol. 11(4). World scientific publishing company, Singapore (1997)
Srihari, S.N.: Recognition of handwritten and machine- printed text for postal address interpretation. Pattern Recognition Letters 14, 291–302 (1993)
Bozinovic, R.M., Srihari, S.N.: Off-line cursive script word recognition. IEEE Trans. Patt. Anal. Mach. Intell. 11, 68–83 (1989)
Buse, R., Liu, Z.Q., Caelli, T.: A structural and relational approach to handwritten word recognition. IEEE Trans. Syst. Man, Cyber. Part B(27), 847–861 (1997)
Mohamed, M., Gader, P.: Handwritten word recognition using segmentation-free hidden Markov modeling and segmentation-based dynamic programming techniques. IEEE Trans. Patt. Anal. Mach. Intell. 18, 548–554 (1996)
Madhavanath, S., Govindaraju, V.: Holistic Lexicon Reduction. In: Proc. Third. Int. Workshop Forntiers in Handwriting Recognition, Buffalo, N.Y, pp. 71–81 (1993)
Madhavanath, S., Govindaraju, V.: Contour-Based Image Processing for Holistic Handwritten Word Recognition. In: Proc. Fourth. Int. Conf. Document Analysis and Recognition (ICDAR 1997), Ulm, Germany (1997)
Madhvanath, S., Krpasundar, V., Govindaraju, V.: Syntactic methodology of pruning large lexicons in cursive script recognition. Pattern-Recognition 34(1) (2001)
Lecolinet, E., Baret, O.: Cursive word recognition: methods and strategies. In: Impedovo, S. (ed.) Fundamentals in Handwriting Recognition. NATO ASI Series F, vol. 24, pp. 235–263. Springer, Heidelberg (1994)
Guillevic, D., Suen, C.Y.: Recognition of legal amounts on bank checks. Pattern-Analysis-and-Applications 1(1), 28–41 (1998)
Paquet, T., Avila, M., Olivier, C.: Word modelling for handwritten word recognition. In: Proceedings Vision Interface 1999. Canadian Image Process. & Pattern Recogniton Soc, Toronto, Ont., Canada, pp. 49–56.
de-Almendra-Freitas, C., El-Yacoubi, A., Bortolozzi, F., Sabourin, R.: Brazilian bank check handwritten legal amount recognition. In: Proceedings 13th Brazilian Symposium on Computer Graphics and Image Processing, pp. 97–104 (2000)
Namane, A., Arezki, M., Guessoum, A., Soubari, E.H., Meyrueis, P., Bruynooghe, M.: Sequential neural network combination for degraded machine-printed character recognition. In: Document Recognition and Retrieval XII, Proc. SPIE, vol. 5676(12), pp. 101–110 (2005)
Namane, A., Guessoum, A., Meyrueis, P.: New skew correction and central zone localization for handwritten word and its application to French legal amounts. In: International Conference on Multimedia, Image Processing and Computer Vision, Madrid, Spain (April 2005) (to be appeared)
Namane, A., Arezki, M., Guessoum, A., Soubari, E.H., Meyrueis, P., Bruynooghe, M.: Off-line unconstrained handwritten numeral character recognition with multiple Hidden Markov models. In: Proceeding of the 4th IASTED International Conference on Visualization, Imaging, and Image Processing, VIIP 2004, pp. 269–276 (2004)
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Namane, A., Guessoum, A., Meyrueis, P. (2005). New Holistic Handwritten Word Recognition and Its Application to French Legal Amount. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Data Mining. ICAPR 2005. Lecture Notes in Computer Science, vol 3686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11551188_72
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DOI: https://doi.org/10.1007/11551188_72
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