Hand written character recognition using star-layered histogram features

In this paper, we present a character recognition method using features extracted from a star layered histogram and trained using neural networks. After several image preprocessing steps, the character region is extracted. Its contour is then used to determine the center of gravity (COG). This CoG point is used as the origin to create a histogram using equally spaced lines extending from the CoG to the contour. The first point the line touches the character represents the first layer of the histogram. If the line extension has not reached the region boundary, the next hit represents the second layer of the histogram. This process is repeated until the line touches the boundary of the character's region. After normalization, these features are used to train a neural network. This method achieves an accuracy of about 93% using the MNIST database of handwritten digits.