A handwritten Bangla numeral recognition scheme based on expanded two-layer SOM

This paper proposes a system for handwritten Bangla numeral recognition based on expanded two-layer self-organising map (SOM), in which every map in the second layer expands from a corresponding neuron in the first layer map. It carries out multiple classifications in the second layer for each character sample. The exact classification of the character image is obtained by a fusion algorithm using confidence coefficients. The discriminability of SOM is improved by this structure. With the directional and density features as the input vector, the experiments on handwritten Bangla numeral samples have achieved satisfactory recognition performance.