A Neural Approach to Cursive Handwritten Character Recognition Using Features Extracted from Binarization Technique-Reference-Cited by-同舟云学术

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A Neural Approach to Cursive Handwritten Character Recognition Using Features Extracted from Binarization Technique

Published:2014-11-30 Issue: Volume: Page:745-771
ISSN:1434-9922
Container-title:Complex System Modelling and Control Through Intelligent Soft Computations
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Author:

Choudhary Amit,Ahlawat Savita,Rishi Rahul

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-319-12883-2_26

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. Historical digit recognition using CNN: a study with English handwritten digits;Sādhanā;2024-01-23

2. Convolutional-Neural-Network-Based Handwritten Character Recognition: An Approach with Massive Multisource Data;Algorithms;2022-04-14

3. Recognition of Compound Characters from Degraded Kannada Documents;Advances in Data Science and Management;2022

4. Digital Recognition of Handwritten Digits Using Convolutional Neural Networks;Algorithms for Intelligent Systems;2022

5. An improved faster-RCNN model for handwritten character recognition;Arabian Journal for Science and Engineering;2021-03-30

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