Optical Character Recognition Using Deep Learning and OpenCV Techniques
DOI:
https://doi.org//10.32628/CSEIT195386Keywords:
Character Segmentation, Convolutional Neural Network, Long Short-Term Memory Networks, Classification.Abstract
The problem of image to text-based conversion is persisting in many areas of applications. This project seeks to classify an individual handwritten character so that handwritten text can be translated to a digital form. We used two main approaches to accomplish this task: classifying digits directly and character segmentation. For the former, we use Convolutional Neural Network (CNN) with various architectures to train a model that can accurately classify characters. For the latter, we use Long Short-Term Memory networks (LSTM) with convolution to construct bounding boxes for each character.
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2019-07-30
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How to Cite
[1]
Shallu Juneja, Dipti Nayan, Rishabh Bhardwaj
, "
Optical Character Recognition Using Deep Learning and OpenCV Techniques, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307,
Volume 5, Issue 4, pp.05-09, July-August-2019. Available at doi : https://doi.org/10.32628/CSEIT195386