Hierarchical Document Structure Analysis: A Tree Construction Approach for Detecting, Ordering, and Reconstructing Document Layouts
The core message of this paper is to propose a comprehensive tree construction based approach, named Detect-Order-Construct, for hierarchical document structure analysis. This approach decomposes the task into three stages: detecting page objects and assigning logical roles, predicting the reading order of the detected objects, and constructing the intended hierarchical structure, including the table of contents.
RoDLA: Benchmarking the Robustness of Document Layout Analysis Models
Document Layout Analysis models' robustness is benchmarked using RoDLA, introducing a taxonomy of perturbations and proposing metrics for evaluation.
LayoutLLM: Large Language Model Instruction Tuning for Visually Rich Document Understanding
Large Language Models (LLMs) integrated with Visual-rich Document Understanding (VrDU) models improve document analysis tasks.
Benchmarking Robustness of Document Layout Analysis Models with RoDLA
Introducing a robustness benchmark for Document Layout Analysis models, proposing metrics to evaluate perturbation impact, and presenting the RoDLA model for improved robust feature extraction.
TextMonkey: A Large Multimodal Model for Document Understanding
TextMonkey is a large multimodal model tailored for text-centric tasks, enhancing document understanding through innovative approaches.
CFRet-DVQA: Coarse-to-Fine Retrieval and Efficient Tuning for Document Visual Question Answering
CFRet-DVQA introduces a retrieval-augmented and efficient tuning framework for Document Visual Question Answering, achieving state-of-the-art results across various datasets.
Transformers and Language Models Revolutionizing Form Understanding: A Comprehensive Review
The authors explore the transformative impact of language models and transformers on form understanding, showcasing their effectiveness in handling noisy scanned documents.
CFRet-DVQA: Coarse-to-Fine Retrieval and Efficient Tuning for Document Visual Question Answering
The author introduces CFRet-DVQA, a framework focusing on retrieval and efficient tuning to enhance Document Visual Question Answering tasks effectively.
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