Abstract-A novel approach for recognition of handwritten compound Bangla characters, along with the Basic characters of Bangla alphabet, is presented here. Compared to English like Roman script, one of the major stumbling blocks in Optical Character Recognition (OCR) of handwritten Bangla script is the large number of complex shaped character classes of Bangla alphabet. In addition to 50 basic character classes, there are nearly 160 complex shaped compound character classes in Bangla alphabet. Dealing with such a large varieties of handwritten characters with a suitably designed feature set is a challenging problem. Uncertainty and imprecision are inherent in handwritten script. Moreover, such a large varieties of complex shaped characters, some of which have close resemblance, makes the problem of OCR of handwritten Bangla characters more difficult. Considering the complexity of the problem, the present approach makes an attempt to identify compound character classes from most frequently to less frequently occured ones, i.e., in order of importance. This is to develop a frame work for incrementally increasing the number of learned classes of compound characters from more frequently occurred ones to less frequently occurred ones along with Basic characters. On experimentation, the technique is observed produce an average recognition rate of 79.25% using MLP and 80.510% using SVM after three fold cross validation of data with future scope of improvement and extension.
In REF , the basic and compound character of handwritten Bangla recognition using Multilayer Perception (MLP) and SVM classifier are achieved around 79.73% and 80.9% accuracy, respectively.
Handwritten Bangla Basic and Compound character recognition using MLP and SVM classifier
Journal of Computing, Volume 2, Issue 2, February 2010, https://sites.google.com/site/journalofcomputing/
Computer Science