Document Image Binarization using LSTM: A Sequence Learning Approach

Author:

Afzal Muhammad Zeshan1,Pastor-Pellicer Joan2,Shafait Faisal3,Breuel Thomas M.4,Dengel Andreas5,Liwicki Marcus1

Affiliation:

1. Technical University of Kaiserslautern, Germany and German Research Center for Artificial Intelligence (DFKI), Germany

2. Departamento de Sistemas Informáticos y Computación Universitat, Politècnica de València València, Spain

3. School of Electrical and Computer Science, NUST, Islamabad, Pakistan

4. Technical University of Kaiserslautern, Germany

5. German Research Center for Artificial Intelligence (DFKI), Germany

Publisher

ACM

Cited by 23 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. TransDocUNet: A Transformer-based UNet Architecture for Degraded Document Image Binarization;Proceedings of the Fourteenth Indian Conference on Computer Vision, Graphics and Image Processing;2023-12-15

2. A hybrid CNN-Transformer model for Historical Document Image Binarization;Proceedings of the 7th International Workshop on Historical Document Imaging and Processing;2023-08-25

3. Scene text understanding: recapitulating the past decade;Artificial Intelligence Review;2023-06-18

4. A Novel Degraded Document Binarization Model through Vision Transformer Network;Information Fusion;2023-05

5. Historical Text Image Enhancement Using Image Scaling and Generative Adversarial Networks;Sensors;2023-04-14

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