  Understanding digital documents is like solving a puzzle, especially
historical ones. Document Layout Analysis (DLA) helps with this puzzle by
dividing documents into sections like paragraphs, images, and tables. This is
crucial for machines to read and understand these documents. In the DL Sprint
2.0 competition, we worked on understanding Bangla documents. We used a dataset
called BaDLAD with lots of examples. We trained a special model called Mask
R-CNN to help with this understanding. We made this model better by
step-by-step hyperparameter tuning, and we achieved a good dice score of 0.889.
However, not everything went perfectly. We tried using a model trained for
English documents, but it didn't fit well with Bangla. This showed us that each
language has its own challenges. Our solution for the DL Sprint 2.0 is publicly
available at https://www.kaggle.com/competitions/dlsprint2/discussion/432201
along with notebooks, weights, and inference notebook.
