Hybrid HMM/ANN Models for Improving Offline Handwritten Text Recognition
In this approach, it proposes the use of hybrid Hidden Markov Model (HMM)/Artificial Neural Network (ANN) models for recognizing the unconstrained offline handwritten texts. Handwritten image normalization from a scanned image includes several steps, usually it begin with image cleaning, page skew correction, and line detection. For handwritten text line image several pre-processing steps to reduce variation in writing style are performed like slope and slant removal and character size normalization. The structural part has been modeled with Markov chains, and a Multilayer Perceptron (MLP) is used to estimate the emission probabilities. This approach presents a new technique to remove slope and slant from the handwritten text and also to normalize the size of text images with supervised learning methods. Slope correction and size normalizations are achieved by classifying the local extrema of text contours with MLP. With the help of Artificial Neural Network slant is removed in a nonuniform way. Experiments have been conducted on many offline handwritten text lines and the recognition rates are achieved.
Keywords: Handwriting recognition, offline handwriting, HMM, hybrid HMM/ANN, neural networks, image normalization, multilayer perceptron
Cite this Article
Varidhi NK, Savitha CK, Prajna MR et al. Hybrid HMM/ANN Models for Improving Offline Handwritten Text Recognition, Journal of Image Processing & Pattern Recognition Progress. 2016; 3(3): 16–23p.
H. Bunke, “Recognition of Cursive Roman Handwriting—Past, Present, and Future,” Proc.Seventh Int’l Conf. Document Analysis and Recognition, vol. 1, pp. 448-459, Aug. 2003.
N. Arica and F. Yarman-Vural, “An Overview of Character Recognition Focused on Off-Line Handwriting,” IEEE Trans. Systems, Man, and Cybernetics, Part C: Applications and Rev., vol. 31, no. 2, pp. 216-233, May 2001.
A. El-Yacoubi, M. Gilloux, R. Sabourin, and C.Y. Suen, “An HMMBased Approach for Off-Line Unconstrained Handwritten Word Modeling and Recognition,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 21, no. 8, pp. 752-760, Aug. 1999.
A. Vinciarelli, S. Bengio, and H. Bunke, “Offline Recognition of Unconstrained Handwritten Texts Using HMMs and Statistical Language Models,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 6, pp. 709-720, June 2004.
M. Schenkel, I. Guyon, and D. Henderson, “On-Line Cursive Script Recognition Using Time Delay Neural Networks and Hidden Markov Models,” Machine Vision and Applications, vol. 8, no. 4, pp. 215-223, 1995.
S. Marinai, M. Gori, and G. Soda, “Artificial Neural Networks for Document Analysis and Recognition,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 1, pp. 23-35, Jan. 2005.
R.M. Bozinovic and S.N. Srihari, “Off-Line Cursive Script Word Recognition,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 11, no. 1, pp. 68-83, Jan. 1989.
P. Simard, D. Steinkraus, and M. Agrawala, “Ink Normalization and Beautification,” Proc. Eighth Int’l Conf. Document Analysis and Recognition, pp. 1182-1187, 2005.
M. Pastor, A. Toselli, and E. Vidal, “Projection Profile Based Algorithm for Slant Removal,” Proc. Int’l Conf. Image Analysis and Recognition, pp. 183-190, 2004.
S. Uchida, E. Taira, and H. Sakoe, “Nonuniform Slant Correction Using Dynamic Programming,” Proc. Sixth Int’l Conf. Document Analysis and Recognition, vol. 1, pp. 434-438, 2001.
W. Francis and H. Kucera, “Brown Corpus Manual, Manual of Information to Accompany a Standard Corpus of Present-Day Edited American English,” technical report, Dept. of Linguistics, Brown Univ., 1979.
L. Bauer, “Manual of Information to Accompany the Wellington Corpus of Written New Zealand English,” technical report, Dept. of Linguistics, Victoria Univ., 1993.
S.J. Young, P.C. Woodland, and W.J. Byrne, “HTK: Hidden Markov Model Toolkit V1.5,” technical report, Cambridge Univ. Eng. Dept. Speech Group and Entropic Research Laboratories, Inc., 1993.
- There are currently no refbacks.