Papers Table Recognition
“Table Recognition” 태그가 달린 논문 60편 · 필터 해제
LLM-as-a-Judge Scores Are Unreliable Optimization Signals in Closed-Loop Table Recognition
LLM-as-a-judge is widely used to provide feedback and selection signals in closedloop regeneration, but this use remains insufficiently validated. We study it in table recognition, where deterministic TEDS evaluation pro…
Table RecognitionRevisiting Structural Dependency in Autoregressive Multi-Task Table Recognition via Order-Independent Cell-Level Representations
Multi-task table recognition jointly addresses table structure prediction, cell localization, and cell content recognition within a unified framework. Existing approaches often rely on autoregressive decoders to generate…
Table RecognitionTableSeq: Unified Generation of Structure, Content, and Layout
We present TableSeq, an image-only, end-to-end framework for joint table structure recognition, content recognition, and cell localization. The model formulates these tasks as a single sequence-generation problem: one de…
Table RecognitionDenTab: A Dataset for Table Recognition and Visual QA on Real-World Dental Estimates
Tables condense key transactional and administrative information into compact layouts, but practical extraction requires more than text recognition: systems must also recover structure (rows, columns, merged cells, heade…
Visual Question AnsweringTable RecognitionTDATR: Improving End-to-End Table Recognition via Table Detail-Aware Learning and Cell-Level Visual Alignment
Tables are pervasive in diverse documents, making table recognition (TR) a fundamental task in document analysis. Existing modular TR pipelines separately model table structure and content, leading to suboptimal integrat…
Table RecognitionYoutu-Parsing: Perception, Structuring and Recognition via High-Parallelism Decoding
This paper presents Youtu-Parsing, an efficient and versatile document parsing model designed for high-performance content extraction. The architecture employs a native Vision Transformer (ViT) featuring a dynamic-resolu…
Table RecognitionHierarchical Modeling Approach to Fast and Accurate Table Recognition
The extraction and use of diverse knowledge from numerous documents is a pressing challenge in intelligent information retrieval. Documents contain elements that require different recognition methods. Table recognition t…
Information RetrievalMulti-Task LearningTable RecognitionTRivia: Self-supervised Fine-tuning of Vision-Language Models for Table Recognition
Table recognition (TR) aims to transform table images into semi-structured representations such as HTML or Markdown. As a core component of document parsing, TR has long relied on supervised learning, with recent efforts…
Table RecognitionLogics-Parsing Technical Report
Recent advances in Large Vision-Language models (LVLM) have spurred significant progress in document parsing task. Compared to traditional pipeline-based methods, end-to-end paradigms have shown their excellence in conve…
Reinforcement LearningTable RecognitionzERExtractor:An Automated Platform for Enzyme-Catalyzed Reaction Data Extraction from Scientific Literature
The rapid expansion of enzyme kinetics literature has outpaced the curation capabilities of major biochemical databases, creating a substantial barrier to AI-driven modeling and knowledge discovery. We present zERExtract…
Relation ExtractionTable RecognitionActive LearningBenchmarking Multimodal LLMs on Recognition and Understanding over Chemical Tables
Chemical tables encode complex experimental knowledge through symbolic expressions, structured variables, and embedded molecular graphics. Existing benchmarks largely overlook this multimodal and domain-specific complexi…
BenchmarkingDescriptiveQuestion AnsweringTable RecognitionOmniParser V2: Structured-Points-of-Thought for Unified Visual Text Parsing and Its Generality to Multimodal Large Language Models
Visually-situated text parsing (VsTP) has recently seen notable advancements, driven by the growing demand for automated document understanding and the emergence of large language models capable of processing document-ba…
document understandingKey Information ExtractionLarge Language ModelMultimodal Large Language Model+2Enhancing Table Recognition with Vision LLMs: A Benchmark and Neighbor-Guided Toolchain Reasoner
Pre-trained foundation models have recently significantly progressed in structured table understanding and reasoning. However, despite advancements in areas such as table semantic understanding and table question answeri…
Question AnsweringTable RecognitionBenchmarking Table Comprehension In The Wild
Large Language Models (LLMs), while being increasingly dominant on a myriad of knowledge-intensive activities, have only had limited success understanding lengthy table-text mixtures, such as academic papers and financia…
BenchmarkingQuestion AnsweringTable RecognitionSee then Tell: Enhancing Key Information Extraction with Vision Grounding
In the digital era, the ability to understand visually rich documents that integrate text, complex layouts, and imagery is critical. Traditional Key Information Extraction (KIE) methods primarily rely on Optical Characte…
Image to textKey Information ExtractionOptical Character RecognitionOptical Character Recognition (OCR)+2PdfTable: A Unified Toolkit for Deep Learning-Based Table Extraction
Currently, a substantial volume of document data exists in an unstructured format, encompassing Portable Document Format (PDF) files and images. Extracting information from these documents presents formidable challenges …
Deep LearningDocument Layout AnalysisOptical Character RecognitionOptical Character Recognition (OCR)+2VRDSynth: Synthesizing Programs for Multilingual Visually Rich Document Information Extraction
Businesses need to query visually rich documents (VRDs) like receipts, medical records, and insurance forms to make decisions. Existing techniques for extracting entities from VRDs struggle with new layouts or require ex…
Entity LinkingProgram SynthesisTable RecognitionThe Socface Project: Large-Scale Collection, Processing, and Analysis of a Century of French Censuses
This paper presents a complete processing workflow for extracting information from French census lists from 1836 to 1936. These lists contain information about individuals living in France and their households. We aim at…
Table RecognitionMulti-Cell Decoder and Mutual Learning for Table Structure and Character Recognition
Extracting table contents from documents such as scientific papers and financial reports and converting them into a format that can be processed by large language models is an important task in knowledge information proc…
DecoderTable RecognitionSynthesizing Realistic Data for Table Recognition
To overcome the limitations and challenges of current automatic table data annotation methods and random table data synthesis approaches, we propose a novel method for synthesizing annotation data specifically designed f…
Table annotationTable Recognition