Neural network based word-wise handwritten script identification system for Indian postal automation

Postal automation is a topic of research over the last few years. There are many works towards the postal automation in USA, UK, Japan and Australia, but for Indian postal automation there is no significant work. This paper deals with word-wise handwritten script identification for Indian postal automation. In the proposed scheme at first document skew is detected and corrected. Non-text parts are then segmented from the document using run length smoothing algorithm (RLSA). Next, using a piecewise projection method the destination address block (DAB), is at first segmented into lines and then into words. Using water reservoir concept we compute the busy-zone of the word. Finally, using matra/Shirorekha, water reservoir concept based feature, fractal based feature, etc. a neural network (NN) classifier is generated for word-wise Bangla and English scripts identification. Overall accuracy of the proposed system is at present 9 7.62%.