Handwritten Character Recognition using Convolution Neural Networks in Python with Keras
In the field of Deep Learning for Computer Vision, scientists have made many enhancements that helped a lot in the development of millions of smart devices. On the other hand, scientists brought a revolutionary change in the field of image processing and one of the biggest challenges in it is to identify documents in both printed as well as hand-written formats. One of the most widely used techniques for the validity of these types of documents is ‘Character Recognition’. This project seeks to classify an individual handwritten word so that handwritten text can be translated to a digital form. It demonstrates the use of neural networks for developing a system that can recognize handwritten English alphabets. In this system, each English alphabet is represented by binary values that are used as input to a simple feature extraction system, whose output is fed to our neural network system. The CNN approach is used to accomplish this task: classifying words directly and character segmentation. For the former, Convolutional Neural Network (CNN) is used with various architectures to train a model that can accurately classify words. For the latter, Long Short Term Memory networks are used with convolution to construct bounding boxes for each character. We then pass the segmented characters to a CNN for classification, and then reconstruct each word according to the results of classification and segmentation.
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