Document digitization is vital for preserving historical records, efficient
document management, and advancing OCR (Optical Character Recognition)
research. Document Layout Analysis (DLA) involves segmenting documents into
meaningful units like text boxes, paragraphs, images, and tables. Challenges
arise when dealing with diverse layouts, historical documents, and unique
scripts like Bengali, hindered by the lack of comprehensive Bengali DLA
datasets. We improved the accuracy of the DLA model for Bengali documents by
utilizing advanced Mask R-CNN models available in the Detectron2 library. Our
evaluation involved three variants: Mask R-CNN R-50, R-101, and X-101, both
with and without pretrained weights from PubLayNet, on the BaDLAD dataset,
which contains human-annotated Bengali documents in four categories: text
boxes, paragraphs, images, and tables. Results show the effectiveness of these
models in accurately segmenting Bengali documents. We discuss speed-accuracy
tradeoffs and underscore the significance of pretrained weights. Our findings
expand the applicability of Mask R-CNN in document layout analysis, efficient
document management, and OCR research while suggesting future avenues for
fine-tuning and data augmentation.

我们通过使用 Detectron2 库中的先进 Mask R-CNN 模型改进了 Bengali 文档的 DLA 模型的准确性，评估了三种变体，结果表明这些模型在准确分割 Bengali 文档方面非常有效，同时我们强调了预训练权重的重要性，拓展了 Mask R-CNN 在文档布局分析、高效文档管理和 OCR 研究中的适用性，并提出了未来微调和数据增强的发展方向。