Abstract
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Layout Analysis (DLA) datasets has hindered the application of OCR in document transcription, e.g., transcribing historical documents and newspapers.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.
Abstract (translated)
虽然深度学习基于孟加拉文Optical Character Recognition(OCR)技术在过去十年中取得了进展,但缺乏大型文档布局分析(DLA)数据集限制了OCR在文档识别中的应用,例如识别历史文档和报纸等。此外,目前在实践中使用的基于规则的DLA系统无法应对域 variations 和分布外的布局。为此,我们提出了第一个多域大型孟加拉文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份人类标注文档样本,包括文本框、段落、图像和表格的710,000个多边形的标注。通过初步实验基准当前最先进的英语DLA技术的性能,我们证明了我们的数据集在训练基于深度学习的孟加拉文文档数字化模型的有效性。
URL
https://arxiv.org/abs/2303.05325
