Abstract
Handwritten text and character recognition is a challenging task compared to recognition of handwritten numeral and computer printed text due to its large variety in nature. Neural Network based approach provides most reliable performance in handwritten character and text recognition but recognition performance depends upon some important factors like no of training samples, reliable features and no of features per character, training time, variety of handwriting etc. Important features from different types of handwriting are collected and are fed to the neural network for training. More no of features increases testing efficiency but it take longer time to converge the error curve. To reduce this training time effectively proper algorithm should be chosen so that the system provides best train and test efficiency in least possible time that is to provide the system fastest intelligence. In this paper we have used Scaled Conjugate Gradient Algorithm, a second order training algorithm for training of neural network. It provides faster training with excellent test efficiency. A scanned handwritten text is taken as input and character level segmentation is done. Some important and reliable features from each character are extracted and used as input to a neural network for training. When the error level reaches into a satisfactory level (10− 12) weights are accepted for testing a test script. Finally a lexicon matching algorithm solves the minor misclassification problems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Cho, S.-B.: Neural-Network Classifiers for Recognizing Totally Unconstrained Handwritten Numerals. IEEE Trans. on Neural Networks 8, 43–53 (1997)
Verma, B.: A Contour Code Feature Based Segmentation For Handwriting Recognition. In: 7th IAPR International conference on Document Analysis and Recognition, ICDAR 2003, pp. 1203–1207 (2003)
Strathy, N.W., Suen, C.Y., Krzyzak, A.: Segmentation of Handwritten Digits using Contour Features. In: ICDAR 1993, pp. 577–580 (2003)
Casey, R.G., Lecolinet, E.: A Survey of Methods and Strategies in Character Segmentation. IEEE Trans. Pattern Analysis and Machine Intelligence 18, 690–706 (1996)
Fletcher, R., Reeves, C.M.: Function minimization by conjugate gradients. The Computer Journal 7, 149–153 (1964)
Gorgevik, D., Cakmakov, D.: An Efficient Three-Stage Classifier for Handwritten Digit Recognition. In: ICPR, vol. 4, pp. 507–510 (2004)
Fink, G.A., Plotz, T.: On Appearance-Based feature Extraction Methods for Writer-Independent Handwritten Text Recognition. In: Proceedings of the 2005 Eight International Conference on Document Analysis and Recognition (ICDAR 2005), pp. 1520–5263 (2005)
Dunn, C.E., Wang, P.S.P.: Character Segmentation Techniques for Handwritten Text A Survey. In: Proceedings of the IInd International Conference on Pattern Recognition, The Hague, The Netherlands, pp. 577–580 (1992)
Zimmermann, M., Bunke, H.: Optimizing the Integration of a Statistical Language Model in HMM based Offline Handwritten Text Recognition. In: Proceedings of the 17th International Conference on Pattern Recognition (ICPR 2004), pp. 1051–4651 (2004)
Hestenes, M.: Conjugate Direction Methods In Optimization. Springer, New York (1980)
Lee, S.-W.: Off-Line Recognition of Totally Unconstrained Handwritten Numerals Using Multilayer Cluster Neural Network. IEEE Trans. on Pattern Analysis and Machine Intelligence 18, 648–652 (1996)
Gader, P.D., Mohamed, M., Chiang, J.-H.: Handwritten Word Recognition with Character and Inter-Character Neural Networks. IEEE Transactions on Systems, Man, and Cybernetics-Part B:Cybernetics 27, 158–164 (1997)
Blumenstein, Verma, B.: Neural–based solutions for the segmentation and recognition of difficult handwritten words from a benchmark database. In: Proc. 5th International Conference on Document Analysis and Recognition, Bangalore, India, pp. 281–284 (1999)
Chiang, J.-H.: A Hybrid Neural Model in Handwritten Word Recognition. Neural Networks 1I, 337–346 (1998)
Haykin, S.: Neural Networks A comprehensive Foundation, 2nd edn.
Duda, R.O., Hart, P.E., Stock, D.G.: Pattern classification, 2nd edn.
Pratt, W.K.: Digital Image Processing, 3rd edn.
Rajeshkaran, S., Vijayalakshmi Pai, G.A.: Neural Networks, Fuzzy Logic, and Genetic Algorithms Synthesis and Applications. Eastern Economy
Khoda, K.M., Liu, Y., Storey, C.: Generalized Polak –Ribiere Algorithm. Journal of Optimization Theory and Application 75(2) (November 1992)
Verma, B., Lee, H.: A Segmentation based Adaptive Approach for Cursive Hand written Text Recognition Neural Networks. In: International Joint Conference on IJCNN 2007, August 12-17, pp. 2212–2216 (2007), doi:10.1109/IJCNN.2007.4371301
Fletcher, R.: Practical Methods of Optimization. John Wiley & Sons, New York (1975)
Moller, M.F.: A Scaled Conjugate Gradient Algorithm For Fast Supervised Learning. Neural Networks 6, 525–533 (1993)
Blumenstein, M., Verma, B.: Neural-based Solutions for the Segmentation and Recognition of Difficult Handwritten Words from a Benchmark Database. In: Proc. 5th International Conference on Document Analysis and Recognition, Bangalore, India, pp. 281–284 (1999)
Dai, Y.H., Yuan, Y.: Convergence properties of the Beale-Powell restart algorithm. Sci. China Ser. A 41, 1142–1150 (1998)
Pal, U., Sharma, N., Wakabayashi, T., Kimura, F.: Off-line handwritten character recognition of Devanagari script. In: Proceedings of 9th International Conference on Document Analysis and Recognition, vol. 1, pp. 496–500 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chel, H., Majumder, A., Nandi, D. (2011). Scaled Conjugate Gradient Algorithm in Neural Network Based Approach for Handwritten Text Recognition. In: Nagamalai, D., Renault, E., Dhanuskodi, M. (eds) Trends in Computer Science, Engineering and Information Technology. CCSEIT 2011. Communications in Computer and Information Science, vol 204. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24043-0_21
Download citation
DOI: https://doi.org/10.1007/978-3-642-24043-0_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24042-3
Online ISBN: 978-3-642-24043-0
eBook Packages: Computer ScienceComputer Science (R0)