Fateh, Amirreza and Fateh, Mansoor and Abolghasemi, Vahid (2023) Enhancing optical character recognition: Efficient techniques for document layout analysis and text line detection. Engineering Reports, 6 (9). DOI https://doi.org/10.1002/eng2.12832
Fateh, Amirreza and Fateh, Mansoor and Abolghasemi, Vahid (2023) Enhancing optical character recognition: Efficient techniques for document layout analysis and text line detection. Engineering Reports, 6 (9). DOI https://doi.org/10.1002/eng2.12832
Fateh, Amirreza and Fateh, Mansoor and Abolghasemi, Vahid (2023) Enhancing optical character recognition: Efficient techniques for document layout analysis and text line detection. Engineering Reports, 6 (9). DOI https://doi.org/10.1002/eng2.12832
Abstract
In recent years, automatic document and text analysis has gained significant importance, driven by advancements in optical character recognition (OCR) technology and the need for efficient processing of large volumes of printed or handwritten documents. This article specifically focuses on document layout analysis (DLA) and text line detection (TLD), both of which are crucial components of OCR systems. Our objective is to develop an effective method for extracting both textual and non‐textual regions, addressing challenges unique to the Persian (and Persian‐like) language(s). In the DLA stage, we employ deep learning models and a voting system to accurately determine the regions of interest. Additionally, we introduce methods such as optimum font size concepts, angle correction, and a line curvature elimination algorithm in the TLD process to enhance OCR accuracy. Comparative evaluations against state‐of‐the‐art methods demonstrate the superiority of our approach, showcasing a 2.8% improvement in the accuracy of Tesseract‐OCR 5.1.0 (a well‐established commercial OCR system) on the official Iranian newspapers dataset. These findings underscore the importance of addressing DLA and TLD challenges to advance OCR technology for Persian language documents and provide a solid foundation for future research in this domain.
Item Type: | Article |
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Uncontrolled Keywords: | connected component; document layout analysis; font size; line detection; Persian printed |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 27 Feb 2024 13:27 |
Last Modified: | 30 Oct 2024 21:29 |
URI: | http://repository.essex.ac.uk/id/eprint/37448 |
Available files
Filename: Engineering Reports - 2023 - Fateh - Enhancing optical character recognition Efficient techniques for document layout.pdf
Licence: Creative Commons: Attribution 4.0