A New Approach For Segmentation in Handwritten Characters Overlapped a Border by Using Adaptive Double Threshold Binarization with Neuro- Fuzzy Tuning
Itaru Nagayama · IEEJ Transactions on Electronics Information and Systems · 1996
A new approach for segmentation in characters overlapped a border is proposed. OCR processing for handwritten characters in account sheets or postal cards requires segmentation and elimination of a border to avoid the difficulty of character recognition. In order to avoid the difficulty, drop-out colored border for special scanner device or multi-step processing under the document analysis approach are used. When an input image of handwritten character overlapped a border is given, the border is segmented at first step, then contour is extracted as feature pattern. This paper presents a new method which simultaneously execute the eliminating a border overlapping on handwritten character and the extraction of character contuor. The method uses double threshold binarization and neuro- fuzzy approach. Binarization levels are decided by using neuro-fuzzy network according to the quality of images. It is shown that the proposed method is better than conventional methods about performance of eliminating the border and extracting of character contour. Availability of this method is indicated by applying it to handwritten postal character images obtained from postal matters.