Handwritten Character Recognition using Neural Network and Tensor Flow

Author:

Abstract

The paper will describe the best approach to get more than 90% accuracy in the field of Handwritten Character Recognition (HCR). There have been plenty of research done in the field of HCR but still it is an open problem as we are still lacking in getting the best accuracy. In this paper, the offline handwritten character recognition will be done using Convolutional neural network and Tensorflow. A method called Soft Max Regression is used for assigning the probabilities to handwritten characters being one of the several characters as it gives the values between 0 and 1 summing up to 1. The purpose is to develop the software with a very high accuracy rate and with minimal time and space complexity and also optimal.

Publisher

Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP

Subject

Electrical and Electronic Engineering,Mechanics of Materials,Civil and Structural Engineering,General Computer Science

Cited by 8 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Extraction of Character from Visuals and Images Using OpenCV;International Journal of Scientific Research in Computer Science, Engineering and Information Technology;2023-05-15

2. Comparison of Handwritten Recognition Methods on Arabic and Latin Characters;Journal of Studies in Science and Engineering;2022-09-11

3. OCR based medical prescription and report analyzer;AIP Conference Proceedings;2022

4. Transfer learning based handwritten character recognition of tamil script using inception-V3 Model;Journal of Intelligent & Fuzzy Systems;2021-12-24

5. Handwriting Analysis Using Deep Learning Approach for the Detection of Personality Traits;Smart Innovation, Systems and Technologies;2021-10-09

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