Chinese chess character recognition using Direction Feature Extraction and backpropagation

Backpropagation and Direction Feature Extraction (DFE) are proposed in this paper for Chinese chess character recognition. Backpropagation is a feed-forward neural network algorithm designed for learning by examples namely by calculating errors and updating weights in each epoch. DFE is a feature extraction method by iterating and calculating the directons surrounding each pixel in the image to obtain the features. In this research, Chinese chess characters are recognized to obtain the correct amount of each chess character in a package. Due to the complex contour, stroke and pattern of Chinese chess characters, Chinese chess characters are difficult to be recognized by new learners. Both Backpropagation and DFE performance are capable in recognizing Chinese chess characters with good accuracy of 98% for various sets and it is also robust from transition, brightness, image noise and rotation up to 60°.