Hierarchical Behavior-Knowledge Space for Highly Reliable Handwritten Numeral Recognition
IEICE - Transactions on Information and Systems
Digit extraction and recognition from machine printed Gurmukhi documents
Proceedings of the International Workshop on Multilingual OCR
A statistical-topological feature combination for recognition of handwritten numerals
Applied Soft Computing
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The performance of a character recognition system depends heavily on what features are being used. Though many kinds of features have been developed and their test performances on standard database have been reported, there is still room to improve the recognition rate by developing an improved feature. In this paper, we propose a new feature based on DDD (Directional Distance Distribution) information. This new concept regards the input pattern array as being circular. Also it contains very rich information by encoding in one representation both the white/black distribution and the directional distance distribution. A test performed on the CENPARMI handwritten numeral database showed a promising result of 97.3% recognition with a neural network classifier using the DDD feature.