Recognition of handwritten meitei mayek and english alphabets using combination of spatial features

Abstract

Handwritten character recognition is an exciting and challenging topic in the field of pattern recognition because of massive variation in writing style and similar looking characters. Combining two different scripts boost the challenge to another level as each language has a unique peculiarity. The choice of distinguishing feature enhances the accuracy and efficiency of a recognition system. In this paper, we present spatial features based recognition of handwritten Manipuri (Meitei Mayek) and English alphabets. Background directional distribution, projection histogram, and uniform local binary pattern features have been used for extracting distinct feature for recognition by KNN classifier. The highest accuracy achieved in the proposed methodology is 92.40%.