Size Invariant Handwritten Character Recognition using Single Layer Feedforward Backpropagation Neural Networks

Handwritten character recognition is among the most challenging research areas in pattern recognition and image processing. With everything going digital, applications of handwritten character recognition are emerging in offices, educational institutes, healthcare units and banks etc., where the documents that are handwritten are dealt more frequently. In this paper, a recognition system based on neural network that follows offline handwritten characters has been proposed for Latin digits and alphabets. Each of the characters that are extracted through query image is then resized dynamically to 60x40 pixels' size and is then passed to the neural networks for the process of recognition. Dynamic resizing enables size invariance in the proposed system and also maintains the aspect ratio of the character so that the image is not distorted during resizing. Neural networks are trained with 19,422 English alphabets' sample and 7,720 digits' sample that are written through 150 different writers in various styles of handwriting. Experimental study realized very encouraging results which are compared with the modern methods on this subject corridor.