Machine Learning Methods for Optical Character Recognition

Success of optical character recognition depends on a number of factors, two of which are feature extraction and classi cation algorithms. In this paper we look at the results of the application of a set of classi ers to datasets obtained through various basic feature extraction methods.

[1]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Aiko M. Hormann,et al.  Programs for Machine Learning. Part I , 1962, Inf. Control..

[3]  Chi-Keung Tang,et al.  A Computational Framework for Feature Extraction and Segmentation , 2000 .

[4]  Nicu Sebe,et al.  Machine Learning in Computer Vision , 2006, Computational Imaging and Vision.

[5]  Milan Sonka,et al.  Image processing analysis and machine vision [2nd ed.] , 1999 .

[6]  Milan Sonka,et al.  Image Processing, Analysis and Machine Vision , 1993, Springer US.

[7]  Ian H. Witten,et al.  Data mining - practical machine learning tools and techniques, Second Edition , 2005, The Morgan Kaufmann series in data management systems.