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.
TextMonkey: Enhancing Document Understanding with Large Multimodal Models
TextMonkey introduces innovative techniques like Shifted Window Attention and Token Resampler to enhance document understanding through large multimodal models.
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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