Handwritten digit recognition using image processing and neural networks

This working prototype system can detect handwritten digits from a scanned image of an input form by using Neural network technique. Hand writing recognition and Image detection through this methodology is very fast and effective as compared to old fashioned image pixel comparison methodology, which is comparably slow. In the initial phase for handwritten digit input we have designed a form which can take hand writing samples from different people. The form must have specific format so user can give multiple input in 10 rows, and hand write 0,1,2,3,4,5,6,7,8,9 in the corresponding sampling cells (rows* columns). The cell must also have width according to your requirement (e.g set it to 20*2 pixels). Once the blank forms have been manually filled by different people then scan these forms with the help of scanner. So now we have images of hand writing samples of digits. In the 2nd phase, we use image slicing technique to slice sample image of size 16*16 pixel for each digit from the scanned form [1]. Each scanned form image will make nearly 100 images of 16*16 pixels. Repeat the same step for all scanned sample forms and place all these 16*16 pixel images (sample pool) into one location. In the detection phase, a three-layered neural network is used: After training, the obtained weight and bias are stored for each digit sequence(signature). It is now possible to identify the meaning of any hand written digit with the help of AT engine. So now when ever any handwritten digit will be given as sample input in to the system, the output array will automatically give the digit whose corresponding match value is detected. The above process is a blueprint of human cognitive thinking process.