A segmentation-free recognition of handwritten touching numeral pairs using modular neural network

The conventional approach to the recognition of handwritten touching numera l pairs uses a process with two steps; splitting the touching numerals and recognizing individual numerals. It shows a limitation mainly due to a larg e variation in touching styles between two numerals. In this paper, we adop t 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 1 00 classes and using a sufficient amount by extracting actual samples from a numeral database and synthesizing samples with a scheme of forcing two nu merals to touch. The experimental results show a promising performance.