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.

翻译：理解数字文档如同解谜，尤其是历史文献。文档布局分析（DLA）通过将文档划分为段落、图像和表格等区域来辅助解谜，这对机器读取和理解文档至关重要。在DL Sprint 2.0竞赛中，我们致力于理解孟加拉语文档，使用包含大量样本的BaDLAD数据集训练了名为Mask R-CNN的专用模型。通过逐步超参数调优，我们优化了该模型，最终获得0.889的良好Dice分数。然而并非一切顺利：我们尝试使用针对英语文档训练的模型，但其并不适用于孟加拉语，这表明每种语言都面临独特挑战。我们在DL Sprint 2.0中的解决方案（含代码、权重及推理笔记本）已公开于https://www.kaggle.com/competitions/dlsprint2/discussion/432201。