DocBank: A Benchmark Dataset for Document Layout Analysis
Document layout analysis usually relies on computer vision models to understand documents while ignoring textual information that is vital to capture. Meanwhile, high quality labeled datasets with both visual and textual information are still insufficient. In this paper, we present \textbf{DocBank}, a benchmark dataset that contains 500K document pages with fine-grained token-level annotations for document layout analysis. DocBank is constructed using a simple yet effective way with weak supervision from the \LaTeX{} documents available on the arXiv.com. With DocBank, models from different modalities can be compared fairly and multi-modal approaches will be further investigated and boost the performance of document layout analysis. We build several strong baselines and manually split train/dev/test sets for evaluation. Experiment results show that models trained on DocBank accurately recognize the layout information for a variety of documents. The DocBank dataset is publicly available at \url{https://github.com/doc-analysis/DocBank}.
Code (2)
Tasks
Document Layout AnalysisMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis
Accurate document layout analysis is a key requirement for high-quality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models hav…
Document Layout AnalysisObject DetectionVision Grid Transformer for Document Layout Analysis
Document pre-trained models and grid-based models have proven to be very effective on various tasks in Document AI. However, for the document layout analysis (DLA) task, existing document pre-trained models, even those p…
Document AIDocument Layout Analysisdocument understandingOptical Character Recognition (OCR)IndicDLP: A Foundational Dataset for Multi-Lingual and Multi-Domain Document Layout Parsing
Document layout analysis is essential for downstream tasks such as information retrieval, extraction, OCR, and digitization. However, existing large-scale datasets like PubLayNet and DocBank lack fine-grained region labe…
Document Layout AnalysisInformation RetrievalA Benchmark of PDF Information Extraction Tools using a Multi-Task and Multi-Domain Evaluation Framework for Academic Documents
Extracting information from academic PDF documents is crucial for numerous indexing, retrieval, and analysis use cases. Choosing the best tool to extract specific content elements is difficult because many, technically d…
RetrievalTable ExtractionBaDLAD: A Large Multi-Domain Bengali Document Layout Analysis Dataset
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 docu…
BenchmarkingDeep LearningDocument Layout AnalysisOptical Character Recognition+1