Extraction of Information from Handwriting using Optical Character recognition and Neural Networks

Image Processing is a vital tool when one is dealing with several images and wishes to perform several complex actions on the same. With advances in technologies, one can now compress, manipulate, extract required information, etc. from any image one wants to. One such application of Image processing is detecting handwritten text and converting it to a digital text format. The main objective is to bridge the gap between the actual bit of paper and the digital world and in doing so, one can operate on the digital data much faster as compared to the actual data. Hence, in this paper, we aim to implement the detection of handwritten text via Optical Character Recognition (OCR). The entire paper will be implemented on TensorFlow. This research work has also analyzed various results and taken appropriate dataset to train the model. Further, the importance of this paper lies in the fact that it can facilitate and open various unexplored avenues. The key novelty of the paper lies in the fact that the data-set used is comprehensive which helps us to produce better result. In addition, the paper successfully analyzes handwritten scripts and extracts it in digital form. Analyzing the text can help combat forgery, understand certain temperaments of the person writing the text, and so on. Coupled with this, this paper has successfully implemented an improved version as compared to the pre-existing solutions by using the convergence of convoluted neural networks (CNN) and the Recurrent Neural Network (RNN).