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.

利用 YOLOv8 模型和创新的后处理技术，本文着重于增强孟加拉文档布局分析（DLA），通过采用数据增强方法解决复杂孟加拉文字符的独特挑战，并通过细致的验证集评估和基于完整数据集的微调来实现准确的元素分割。我们的组合模型结合后处理优于单个基本架构，解决了 BaDLAD 数据集中发现的问题。通过利用这种方法，我们旨在推进孟加拉文档分析，从而提高 OCR 和文档理解，BaDLAD 成为这一努力的基础资源，有助于未来的研究。此外，我们的实验为将新策略纳入已经建立的解决方案提供了关键见解。

基于后处理的孟加拉文档布局分析与 YOLOV8

A Post-Processing Based Bengali Document Layout Analysis with YOLOV8

In the rapidly evolving digital era, the analysis of document layouts plays a
pivotal role in automated information extraction and interpretation. In our
work, we have trained MViTv2 transformer model architecture with cascaded mask
R-CNN on BaDLAD dataset to extract text box, paragraphs, images and tables from
a document. After training on 20365 document images for 36 epochs in a 3 phase
cycle, we achieved a training loss of 0.2125 and a mask loss of 0.19. Our work
extends beyond training, delving into the exploration of potential enhancement
avenues. We investigate the impact of rotation and flip augmentation, the
effectiveness of slicing input images pre-inference, the implications of
varying the resolution of the transformer backbone, and the potential of
employing a dual-pass inference to uncover missed text-boxes. Through these
explorations, we observe a spectrum of outcomes, where some modifications
result in tangible performance improvements, while others offer unique insights
for future endeavors.

在数字化快速发展的时代，文档布局的分析在自动化信息提取和解释中起着重要作用。通过在 BaDLAD 数据集上训练 MViTv2 转换器模型架构与级联掩膜 R-CNN，我们从文档中提取了文本框、段落、图像和表格。通过对 20365 个文档图像进行 36 个周期的训练，在 3 个阶段的循环中，我们取得了 0.2125 的训练损失和 0.19 的掩膜损失。同时，我们还探讨了旋转和翻转增强、在推理前对输入图像进行切片、改变转换器主干的分辨率以及使用双通道推理来发现遗漏的文本框等潜在的增强方法。通过这些探索，我们观察到一系列结果，一些修改带来了实质性的性能改进，而其他的修改为未来的努力提供了独特的见解。