Framework and Model Analysis on Bengali Document Layout Analysis Dataset: BaDLAD
This work addresses the problem of analyzing complex layouts in Bengali documents, which could be useful for other languages, but it is incremental as it applies existing methods to a new dataset.
The study tackled Bengali document layout analysis by evaluating Detectron2, YOLOv8, and SAM on the BaDLAD dataset, finding that Detectron2 excels at segmenting text boxes and paragraphs, YOLOv8 is effective for tables and pictures, and SAM handles tricky layouts, with comparisons based on accuracy and speed.
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.