Framework and Model Analysis on Bengali Document Layout Analysis Dataset: BaDLAD

TL;DR

This paper compares the effectiveness of Detectron2, YOLOv8, and SAM in analyzing Bengali document layouts, providing insights into their accuracy and speed for different layout components.

Contribution

It introduces a comprehensive analysis of multiple computer vision models applied to Bengali document layout understanding, highlighting their strengths and limitations.

Findings

Detectron2 excels at segmenting document parts

YOLOv8 effectively identifies tables and images

SAM aids in understanding complex layouts

Abstract

This study focuses on understanding Bengali Document Layouts using advanced computer programs: Detectron2, YOLOv8, and SAM. We looked at lots of different Bengali documents in our study. Detectron2 is great at finding and separating different parts of documents, like text boxes and paragraphs. YOLOv8 is good at figuring out different tables and pictures. We also tried SAM, which helps us understand tricky layouts. We tested these programs to see how well they work. By comparing their accuracy and speed, we learned which one is good for different types of documents. Our research helps make sense of complex layouts in Bengali documents and can be useful for other languages too.