Handwritten Bangla character recognition in machine-printed forms using gradient information and Haar wavelet

A robust and efficient algorithm to recognize handwritten Bangla (Bengali) characters in machine-printed forms is proposed. It is based on the combination of gradient features and Haar wavelet coefficients. The gradient feature is used to capture local characteristics, and for its sensitivity to the usual deformation and idiosyncrasy of handwritten characters, wavelet transform is used for multi-resolution analysis of character images. Such a strategy with combined features captures adequate global characteristics in different scales. Two feature-combination schemes are devised and tested on test images of 4372 instances of 49 characters and 10 numerals, after being trained by a set of 59×25 = 1475 images. Finally, a k-NN classifier is used for the character recognition, which shows 87.65% and 88.95% recognition accuracies for the two schemes.