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，尤其是历史文档。文档布局分析（DLA）通过将文档分割为段落、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，使用了包含大量样本的BaDLAD数据集，并训练了名为Mask R-CNN的专用模型。通过逐步超参数调优，我们使模型性能得到提升，最终获得0.889的优异Dice分数。然而某些环节未达预期：尝试使用面向英文文档训练的模型时，其在孟加拉语场景的适配性不佳——这证明每种语言都存在独特挑战。我们的DL Sprint 2.0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。