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Table Recognition
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Table recognition refers to the process of automatically identifying and extracting tabular structures from unstructured data sources such as text documents, images, or scanned documents. The goal of table recognition is to accurately detect the presence of tables within the data and extract their contents, including rows, columns, headers, and cell values.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
OmniParser V2: Structured-Points-of-Thought for Unified Visual Text Parsing and Its Generality to Multimodal Large Language Models
Enhancing Table Recognition with Vision LLMs: A Benchmark and Neighbor-Guided Toolchain Reasoner
Benchmarking Table Comprehension In The Wild
See then Tell: Enhancing Key Information Extraction with Vision Grounding
PdfTable: A Unified Toolkit for Deep Learning-Based Table Extraction
VRDSynth: Synthesizing Programs for Multilingual Visually Rich Document Information Extraction
The Socface Project: Large-Scale Collection, Processing, and Analysis of a Century of French Censuses
Multi-Cell Decoder and Mutual Learning for Table Structure and Character Recognition
Synthesizing Realistic Data for Table Recognition
TC-OCR: TableCraft OCR for Efficient Detection & Recognition of Table Structure & Content
OmniParser: A Unified Framework for Text Spotting, Key Information Extraction and Table Recognition
UniTable: Towards a Unified Framework for Table Recognition via Self-Supervised Pretraining
Enhancement of Bengali OCR by Specialized Models and Advanced Techniques for Diverse Document Types
A Review On Table Recognition Based On Deep Learning
High-Performance Transformers for Table Structure Recognition Need Early Convolutions
A large-scale dataset for end-to-end table recognition in the wild
An End-to-End Multi-Task Learning Model for Image-based Table Recognition
Rethinking Image-based Table Recognition Using Weakly Supervised Methods
LORE: Logical Location Regression Network for Table Structure Recognition
StrucTexTv2: Masked Visual-Textual Prediction for Document Image Pre-training
Aligning benchmark datasets for table structure recognition
Deep learning for table detection and structure recognition: A survey
RegCLR: A Self-Supervised Framework for Tabular Representation Learning in the Wild
PP-StructureV2: A Stronger Document Analysis System
TRUST: An Accurate and End-to-End Table structure Recognizer Using Splitting-based Transformers
TSRFormer: Table Structure Recognition with Transformers
Robust Table Detection and Structure Recognition from Heterogeneous Document Images
Neural Collaborative Graph Machines for Table Structure Recognition
PubTables-1M: Towards comprehensive table extraction from unstructured documents
Detecting Layout Templates in Complex Multiregion Files
Split, embed and merge: An accurate table structure recognizer
TGRNet: A Table Graph Reconstruction Network for Table Structure Recognition
ICDAR 2021 Competition on Scientific Literature Parsing
ICDAR 2021 Competition on Scientific Table Image Recognition to LaTeX
Flexible Table Recognition and Semantic Interpretation System
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