Handwritten Script recognition using Soft Computing

Today, handwritten script recognition is challenging part in the computer science. It is important to know a script used in writing. Script recognitions have many important applications like automatic transcription of multilingual documents, searching document image, script sorting. Proposed work emphasis on the “block level technique” where script recognition recognizes the script of the given document in a mixture of various script documents. There has an important role of computational field like artificial intelligence, expect system. Feature extraction technique is an important step in Script recognition. In this project, we have used combined approach of Discrete Cosine Transform (DCT) and discrete wavelets Transform (DWT) for feature extraction and neural network (feed forward back propagation) classifier for classification and recognition purpose. Human mind can easily trace handwritten script so there have we use Artificial intelligence in which we use classifier neural network. The proposed system has been experimented on three handwritten scripts Hindi, English and Urdu. Our database contains 961 handwritten samples, written in three scripts. Every script (Hindi, English and Urdu) contains 320 samples (160 samples are written in small font and another 160 samples are in large font).