Handwritten Character Recognition using Convolution Neural Networks in Python with Keras

  • Hanu Priya Indiran
Keywords: Computer Vision , CNN, Character Recognition, Classification, Deep Learning, Neural Networks

Abstract

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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Published
2020-03-26
How to Cite
Indiran, H. P. (2020). Handwritten Character Recognition using Convolution Neural Networks in Python with Keras. Asian Journal For Convergence In Technology (AJCT), 5(3), 123-131. Retrieved from http://asianssr.org/index.php/ajct/article/view/946