Handwritten Word Detection Based on Machine Learning Approach

Abstract

This thesis presents “Handwritten word detection based on machine learning approach”. Handwritten focus is turning into a necessary problem in a number of years however it turns into an assignment to get suitable overall performance due to the alignment and many of them are similar. The mapping is represented as a deep convolutional neural network (CNN) that takes the low-resolution image as the input and outputs the high resolution one. The agenda for this field is to enable machines to view the world as humans do, perceive it in a similar manner, and even use the knowledge for a multitude of tasks such as Image & Video recognition, Image Analysis & Classification, Media Recreation, Recommendation Systems, Natural Language Processing, etc. Many research works have been done on that topic but most of are only capable of recognizing digits only. Here, try to develop a model called CRNN model (Convolutional Recurrent Neural Network) for detection, recognize and acknowledge handwritten. This system will be capable of various images and next automatically recognize which letter is in the image. In our model, I get 93.06% accuracy for detection character.