While strides have been made in deep learning based Bengali Optical Character Recognition (OCR) in the past decade, the absence of large Document Layout Analysis (DLA) datasets has hindered the application of OCR in document transcription, e.g., transcribing historical documents and newspapers. Moreover, rule-based DLA systems that are currently being employed in practice are not robust to domain variations and out-of-distribution layouts. To this end, we present the first multidomain large Bengali Document Layout Analysis Dataset: BaDLAD. This dataset contains 33,695 human annotated document samples from six domains - i) books and magazines, ii) public domain govt. documents, iii) liberation war documents, iv) newspapers, v) historical newspapers, and vi) property deeds, with 710K polygon annotations for four unit types: text-box, paragraph, image, and table. Through preliminary experiments benchmarking the performance of existing state-of-the-art deep learning architectures for English DLA, we demonstrate the efficacy of our dataset in training deep learning based Bengali document digitization models.

翻译：过去十年间，基于深度学习的孟加拉语光学字符识别（OCR）虽取得进展，但大规模文档布局分析（DLA）数据集的缺失阻碍了OCR在文档转录（如历史文献与报纸转录）中的应用。此外，当前实际部署的基于规则的DLA系统对领域差异与分布外布局缺乏鲁棒性。为此，我们提出了首个多领域大规模孟加拉语文档布局分析数据集BaDLAD。该数据集包含来自六个领域的33,695个人工标注文档样本：i) 书籍与杂志、ii) 公共领域政府文件、iii) 解放战争文献、iv) 报纸、v) 历史报纸、vi) 地契文件，涵盖四种单元类型的71万个多边形标注：文本框、段落、图像和表格。通过初步实验对标现有英语DLA领域最先进的深度学习架构性能，我们验证了该数据集在训练基于深度学习的孟加拉语文档数字化模型中的有效性。