Enhancement of Bengali OCR by Specialized Models and Advanced Techniques for Diverse Document Types
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866909098774102016 |
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| author | Rabby, AKM Shahariar Azad Ali, Hasmot Islam, Md. Majedul Abujar, Sheikh Rahman, Fuad |
| author_facet | Rabby, AKM Shahariar Azad Ali, Hasmot Islam, Md. Majedul Abujar, Sheikh Rahman, Fuad |
| contents | This research paper presents a unique Bengali OCR system with some capabilities. The system excels in reconstructing document layouts while preserving structure, alignment, and images. It incorporates advanced image and signature detection for accurate extraction. Specialized models for word segmentation cater to diverse document types, including computer-composed, letterpress, typewriter, and handwritten documents. The system handles static and dynamic handwritten inputs, recognizing various writing styles. Furthermore, it has the ability to recognize compound characters in Bengali. Extensive data collection efforts provide a diverse corpus, while advanced technical components optimize character and word recognition. Additional contributions include image, logo, signature and table recognition, perspective correction, layout reconstruction, and a queuing module for efficient and scalable processing. The system demonstrates outstanding performance in efficient and accurate text extraction and analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_05158 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancement of Bengali OCR by Specialized Models and Advanced Techniques for Diverse Document Types Rabby, AKM Shahariar Azad Ali, Hasmot Islam, Md. Majedul Abujar, Sheikh Rahman, Fuad Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning This research paper presents a unique Bengali OCR system with some capabilities. The system excels in reconstructing document layouts while preserving structure, alignment, and images. It incorporates advanced image and signature detection for accurate extraction. Specialized models for word segmentation cater to diverse document types, including computer-composed, letterpress, typewriter, and handwritten documents. The system handles static and dynamic handwritten inputs, recognizing various writing styles. Furthermore, it has the ability to recognize compound characters in Bengali. Extensive data collection efforts provide a diverse corpus, while advanced technical components optimize character and word recognition. Additional contributions include image, logo, signature and table recognition, perspective correction, layout reconstruction, and a queuing module for efficient and scalable processing. The system demonstrates outstanding performance in efficient and accurate text extraction and analysis. |
| title | Enhancement of Bengali OCR by Specialized Models and Advanced Techniques for Diverse Document Types |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2402.05158 |