MLPNN Based Handwritten Character Recognition Using Combined Feature Extraction

Statistical techniques for off-line character recognition are not flexible and adaptive enough for new handwriting constraints. Offline handwritten character recognition of English alphabets using a three layered feed forward neural network is presented in this paper. The proposed recognition system describes the evaluation of feed forward neural network by combining four different feature extraction approaches(box approach, diagonal distance approach, mean and gradient operations). The proposed recognition system performs well on the benchmark dataset CEDAR (Centre of Excellence for Document Analysis And Recognition).