Bengali Document Layout Analysis -- A YOLOV8 Based Ensembling Approach
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866909814916907008 |
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| author | Ahmed, Nazmus Sakib Noor, Saad Sakib Sikder, Ashraful Islam Shanto Paul, Abhijit |
| author_facet | Ahmed, Nazmus Sakib Noor, Saad Sakib Sikder, Ashraful Islam Shanto Paul, Abhijit |
| contents | This paper focuses on enhancing Bengali Document Layout Analysis (DLA) using the YOLOv8 model and innovative post-processing techniques. We tackle challenges unique to the complex Bengali script by employing data augmentation for model robustness. After meticulous validation set evaluation, we fine-tune our approach on the complete dataset, leading to a two-stage prediction strategy for accurate element segmentation. Our ensemble model, combined with post-processing, outperforms individual base architectures, addressing issues identified in the BaDLAD dataset. By leveraging this approach, we aim to advance Bengali document analysis, contributing to improved OCR and document comprehension and BaDLAD serves as a foundational resource for this endeavor, aiding future research in the field. Furthermore, our experiments provided key insights to incorporate new strategies into the established solution. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_00848 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Bengali Document Layout Analysis -- A YOLOV8 Based Ensembling Approach Ahmed, Nazmus Sakib Noor, Saad Sakib Sikder, Ashraful Islam Shanto Paul, Abhijit Computer Vision and Pattern Recognition Machine Learning This paper focuses on enhancing Bengali Document Layout Analysis (DLA) using the YOLOv8 model and innovative post-processing techniques. We tackle challenges unique to the complex Bengali script by employing data augmentation for model robustness. After meticulous validation set evaluation, we fine-tune our approach on the complete dataset, leading to a two-stage prediction strategy for accurate element segmentation. Our ensemble model, combined with post-processing, outperforms individual base architectures, addressing issues identified in the BaDLAD dataset. By leveraging this approach, we aim to advance Bengali document analysis, contributing to improved OCR and document comprehension and BaDLAD serves as a foundational resource for this endeavor, aiding future research in the field. Furthermore, our experiments provided key insights to incorporate new strategies into the established solution. |
| title | Bengali Document Layout Analysis -- A YOLOV8 Based Ensembling Approach |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2309.00848 |