SOTAVerified

Table Recognition

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.

Papers

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Showing 1–10 of 50 papers

TitleStatusHype
Benchmarking Multimodal LLMs on Recognition and Understanding over Chemical Tables—0
OmniParser V2: Structured-Points-of-Thought for Unified Visual Text Parsing and Its Generality to Multimodal Large Language ModelsCode0
Enhancing Table Recognition with Vision LLMs: A Benchmark and Neighbor-Guided Toolchain ReasonerCode1
Benchmarking Table Comprehension In The Wild—0
See then Tell: Enhancing Key Information Extraction with Vision Grounding—0
PdfTable: A Unified Toolkit for Deep Learning-Based Table ExtractionCode0
VRDSynth: Synthesizing Programs for Multilingual Visually Rich Document Information Extraction—0
The Socface Project: Large-Scale Collection, Processing, and Analysis of a Century of French Censuses—0
Multi-Cell Decoder and Mutual Learning for Table Structure and Character RecognitionCode1
Synthesizing Realistic Data for Table RecognitionCode0
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All datasetsPubTabNetTable Recognition Challenge mini-testTable Recognition Challenge testICDAR2013 table structure recognitionWTW

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1StrucTexTv2 (small)F178.9—Unverified