Bengali Document Layout Analysis -- A YOLOV8 Based Ensembling Approach

TL;DR

This paper presents a novel ensemble approach using YOLOv8 and post-processing techniques to improve Bengali Document Layout Analysis, addressing script-specific challenges and outperforming existing methods on the BaDLAD dataset.

Contribution

Introduces a two-stage ensemble model with post-processing for Bengali DLA, enhancing accuracy and robustness over individual architectures.

Findings

Outperforms baseline models on BaDLAD dataset

Effective data augmentation improves model robustness

Two-stage prediction enhances element segmentation accuracy

Abstract

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.