  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transcription, e.g., transcribing historical documents and newspapers.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.
