Bangla Numeral Recognition Research Articles

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.

Handwritten Bangla numeral recognition system and its application to postal automation

A recognition system for handwritten Bangla numerals and its application to automatic letter sorting machine for Bangladesh Post is presented. The system consists of preprocessing, feature extraction, recognition and integration. Based on the theories of principal component analysis (PCA), two novel approaches are proposed for recognizing handwritten Bangla numerals. One is the image reconstruction recognition approach, and the other is the direction feature extraction approach combined with PCA and SVM. By examining the handwritten Bangla numeral data captured from real Bangladesh letters, the experimental results show that our proposed approaches are effective. To meet performance requirements of automatic letter sorting machine, we integrate the results of the two proposed approaches with one conventional PCA approach. It has been found that the recognition result achieved by the integrated system is more reliable than that by one method alone. The average recognition rate, error rate and reliability achieved by the integrated system are 95.05%, 0.93% and 99.03%, respectively. Experiments demonstrate that the integrated system also meets speed requirement.