Isolated Handwritten Devnagri Character Recognition using Fourier Descriptor and HMM

This paper describes a complete system for the recognition of isolated handwritten Devnagri character using Fourier Descriptor and Hidden-Markov model (HMM). The HMM has the property that its states are not defined as a priory information, but are determined automatically based on a database of handwritten numerals images. In this work the image database consist of 500 images of handwritten Devnagri characters from 50 different writers. Before extracting the features, the images are normalized using image isometrics such as translation, rotation and scaling. After normalization the Fourier features are extracted using Fourier Descriptor. An automatic system trained 400 images of image database and character model form with multivariate Gaussian state conditional distribution. A separate set of 100 characters was used to test the system. The recognition accuracy for individual character varies from 90% to 100% for number of states per model N=3 and 100% for N=5