Abstract:
Numerous associations still rely on paper-escalated work processes. Due to the fact that the printed and handwritten documents are all around acknowledged and perceived for any authoritative report. The major issues having handled the paper documents are the inability to monitor the lost data, storage and money and time wasted on re-keying data. It is possible to address these problems through a solution that can digitize the data in these paper documents. The most common approach is to identify handwritten of a single person through a template matching approach. In the proposed approach, the template of the document is identified and handwritten areas are extracted through an image processing component and the identification of the handwritten characters are addressed through training the system using a convolutional neural network. The accuracy level of 90% achieved with recognition of form template and 84.67% accuracy level achieved with handwritten character recognition.
