Amharic Character Recognition Based on Features Extracted by CNN and Auto-Encoder Models-Reference-Cited by-同舟云学术

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Amharic Character Recognition Based on Features Extracted by CNN and Auto-Encoder Models

Published:2021-06-25 Issue: Volume: Page:
ISSN:
Container-title:2021 The 13th International Conference on Computer Modeling and Simulation
language:
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Author:

Yohannes Obsie Efrem¹,Qu Hongchun¹,Huang Qingqin¹

Affiliation:

1. Chongqing University of Posts and Telecommunications, China

Funder

National Natural Science Foundation of China, Program of Basic Research and Frontier Technology of Chongqing Municipal Science Commission

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3474963.3474972

Reference44 articles.

1. Optical character recognition of Amharic documents;Meshesha M.;African J. Inf. Commun. Technol.,2007

2. Dyadic Multi-resolution Analysis-Based Deep Learning for Arabic Handwritten Character Classification

3. Image character recognition using deep convolutional neural network learned from different languages

4. Offline handwritten English character recognition based on convolutional neural network

5. High-Performance OCR for Printed English and Fraktur Using LSTM Networks

Cited by 4 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. Handwritten Amharic Word Recognition With Additive Attention Mechanism;IEEE Access;2024

2. Improving Amharic Handwritten Word Recognition Using Auxiliary Task;Third Congress on Intelligent Systems;2023

3. End-to-End Historical Handwritten Ethiopic Text Recognition Using Deep Learning;IEEE Access;2023

4. Multi-script handwritten digit recognition using multi-task learning;Journal of Intelligent & Fuzzy Systems;2022-06-01

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