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Layout Analysis (DLA) datasets has hindered the application of OCR in document transcription, e.g., transcribing historical documents and newspapers.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：摘要：尽管过去十年中基于深度学习的孟加拉语光学字符识别（OCR）取得了进展，但缺乏大型文档布局分析（DLA）数据集阻碍了OCR在文档转录中的应用，例如历史文献和报纸的转录。此外，当前实践中采用的基于规则的DLA系统对领域变化和分布外布局并不鲁棒。为此，我们提出了首个多领域大型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) 房地产契约，共包含71万个针对四种单元类型（文本框、段落、图像和表格）的多边形标注。通过基准测试现有最先进的英文DLA深度学习架构性能的初步实验，我们证明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方面的有效性。