Open Access Open Access  Restricted Access Subscription or Fee Access

Off-Line Handwritten Character Recognition with Hidden Markov Models

Dr.G.M. Nasira, P. Banumathi

Abstract


Handwritten Character recognition is a process, which associates a symbolic meaning with letters, symbols and numbers drawn on an image. Many researches have been done to solve handwritten character recognition in the areas such as Image Processing, Pattern Recognition, and Artificial Intelligence etc. Recognition of offline handwritten character is a goal of much research effort in pattern recognition. Many techniques have been applied for recognition of handwritten characters but still it is the case of less efficiency and accuracy of recognition. Thus this paper brings out a complete system to recognize offline handwritten characters using Hidden Markov Model (HMM), in which an artificial neural network is trained to identify similarities and patterns among different handwritten samples with high accuracy. HMM has a Freedom to manipulate the training and verification processes. HMMs are very powerful modeling tools than many statistical methods

Keywords


Artificial Neural Network (ANN), Feature extraction, Handwritten character recognition, Hidden Markov Model (HMM), Preprocessing, Segmentation

Full Text:

PDF

References


Jagadeesh Kannan R, Prabhakar R “Off-Line Cursive Handwritten Tamil Character Recognition” Wseas Transactions on Signal Processing, Vol. 4, June 2008.

Prof. Dr. Venkatesh J, Sureshkumar C “Handwritten Tamil Character Recognition using Support Vector Machines.” IJCNS International Journal of Computer and Network Security, Vol. 1, No. 3, December 2009.

Plamondon.R., and S.Srihari “Online and offline Handwriting Recognition : A Comprehensive Survey.” IEEE Trans. On Pattern Analysis and Machine Intelligence, Vol. 22 No.1, 2000, pp 63-84..

Bunke.H,Roth.M, and E.G. Schukat Talamazzini “Offline Cursive Handwriting Recognition using Hidden Markov Models”. Pattern Recognition, Vol.28 No. 9, 1995, pp 1399-1413.

Teresa M. Przytycka, “Encyclopedia of The Human Genome: Hidden Markov Models.” USA: Nature Publishing Group, 2007.

Y.He, A.Kundu : “2-D shape classification Using Hidden Markov Model”, IEEE Trans. On PAMI, vol.13,1991,pp.1172-1184.

Samaria. F, F. Fallside : “Face Identification and Feature Extraction using Hidden Markov Models”, in G. Vernazza, A. N. Venetsanopoulos, C. Braccini (editors): Image Processing: Theory and applications, Elsevier Science publishers B.V., 1993, pp.292-302.

Rabiner L.R, “A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition,” Proceeding of the IEEE, vol 77, pp. 257-286, 1989.

Roongroj Nopsuwanchai , and Dan Povey , “Discriminative Training for HMM-Based Offline Handwritten Character Recognition”. IEEE in the Seventh International Conference on Document Analysis and Recognition (ICDAR 2003).

Shashank Mathur, Vaibhav Aggarwal, Himanshu Joshi, Anil Ahlawat, “Off-Line Handwriting Recognition using Genetic Algorithm”, International Book Series Information Science and Computing, June – July 2008.

Wang, Patrick Shen-Pei, “Learning, Representation, Understanding and Recognition of Words – An Intelligent Approach”, Fundamentals in Handwriting Recognition. Ed.Sebastiano Impedovo, New Yark, Springer Verlag, 1994.

Skapura, David M, “Building Neural Networks”, ACM Press, New York, pp. 29-33.

Anil K. Jain, Jianchang Mao, K.M.Mohiuddin, Artificial Neural Networks, A Tutorial , Computer, Vol,29, n-3, p.31-44, March 1996.


Refbacks

  • There are currently no refbacks.