Segmentation of connected handwritten digits using Self-Organizing Maps

• We present a new method for the segmentation of connected handwritten digits.
• It is based on feature points extraction and selection by Self-Organizing Maps.
• The proposed algorithm may map each touching region between the digits.
• The proposed method overperformed other state-of-the-art algorithms.

Segmentation is an important issue in document image processing systems as it can break a sequence of characters into its components. Its application over digits is common in bank checks, mail and historical document processing, among others. This paper presents an algorithm for segmentation of connected handwritten digits based on the selection of feature points, through a skeletonization process, and the clustering of the touching region via Self-Organizing Maps. The segmentation points are then found, leading to the final segmentation. The method can deal with several types of connection between the digits, having also the ability to map multiple touching. The proposed algorithm achieved encouraging results, both relating to other state-of-the-art algorithms and to possible improvements.