A SEGMENTATION-FREE RECOGNITION OF HANDWRITTEN TOUCHING NUMERAL PAIRS USING MODULAR NEURAL NETWORK

Abstract

The conventional approach to the recognition of handwritten touching numeral pairs uses a process with two steps; splitting the touching numerals and recognizing individual numerals. It shows a limitation mainly due to a large variation in touching styles between two numerals. In this paper, we adopt the segmentation-free approach, which regards a touching numeral pair as an atomic pattern. Two important issues are raised, i.e. solving the large-set classification and constructing a large-size training set. For the 100-class classification, we use a modular neural network which consists of 100 separate subnetworks. We construct the training set with a balance among 100 classes and using a sufficient amount by extracting actual samples from a numeral database and synthesizing samples with a scheme of forcing two numerals to touch. The experimental results show a promising performance.