# Optimized Table Tokenization for Table Structure Recognition

arXiv: https://arxiv.org/abs/2305.03393 · HTML: https://mlsanity.com/paper/2305.03393/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 24 references that are in the map.
- First posted on arXiv 5 May 2023.

## Builds on (most cited first, 15 of 15)

- [PubLayNet: largest dataset ever for document layout analysis](https://mlsanity.com/paper/1908.07836/) (2019)
- [Image-based table recognition: data, model, and evaluation](https://mlsanity.com/paper/1911.10683/) (2019)
- [DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis](https://mlsanity.com/paper/2206.01062/) (2022)
- [Global Table Extractor (GTE): A Framework for Joint Table Identification and Cell Structure Recognition Using Visual Context](https://mlsanity.com/paper/2005.00589/) (2020)
- [PubTables-1M: Towards comprehensive table extraction from unstructured documents](https://mlsanity.com/paper/2110.00061/) (2021)
- [CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documents](https://mlsanity.com/paper/2004.12629/) (2020)
- [TableFormer: Table Structure Understanding with Transformers](https://mlsanity.com/paper/2203.01017/) (2022)
- [Complicated Table Structure Recognition](https://mlsanity.com/paper/1908.04729/) (2019)
- [Split, embed and merge: An accurate table structure recognizer](https://mlsanity.com/paper/2107.05214/) (2021)
- [PingAn-VCGroup's Solution for ICDAR 2021 Competition on Scientific Literature Parsing Task B: Table Recognition to HTML](https://mlsanity.com/paper/2105.01848/) (2021)
- [TGRNet: A Table Graph Reconstruction Network for Table Structure Recognition](https://mlsanity.com/paper/2106.10598/) (2021)
- [Corpus Conversion Service: A Machine Learning Platform to Ingest Documents at Scale](https://mlsanity.com/paper/1806.02284/) (2018)
- [Robust PDF Document Conversion Using Recurrent Neural Networks](https://mlsanity.com/paper/2102.09395/) (2021)
- [Delivering Document Conversion as a Cloud Service with High Throughput and Responsiveness](https://mlsanity.com/paper/2206.00785/) (2022)
- [Tables to LaTeX: structure and content extraction from scientific tables](https://mlsanity.com/paper/2210.17246/) (2022)

## Built on by (most cited first, 30 of 34)

- [MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing](https://mlsanity.com/paper/2509.22186/) (2025)
- [SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversion](https://mlsanity.com/paper/2503.11576/) (2025)
- [Docling Technical Report](https://mlsanity.com/paper/2408.09869/) (2024)
- [Granite Vision: a lightweight, open-source multimodal model for enterprise Intelligence](https://mlsanity.com/paper/2502.09927/) (2025)
- [UniTable: Towards a Unified Framework for Table Recognition via Self-Supervised Pretraining](https://mlsanity.com/paper/2403.04822/) (2024)
- [Youtu-Parsing: Perception, Structuring and Recognition via High-Parallelism Decoding](https://mlsanity.com/paper/2601.20430/) (2026)
- [TFLOP: Table Structure Recognition Framework with Layout Pointer Mechanism](https://mlsanity.com/paper/2501.11800/) (2025)
- [SEMv3: A Fast and Robust Approach to Table Separation Line Detection](https://mlsanity.com/paper/2405.11862/) (2024)
- [SPRINT: Script-agnostic Structure Recognition in Tables](https://mlsanity.com/paper/2503.11932/) (2025)
- [Advanced Layout Analysis Models for Docling](https://mlsanity.com/paper/2509.11720/) (2025)
- [ScreenParse: Moving Beyond Sparse Grounding with Complete Screen Parsing Supervision](https://mlsanity.com/paper/2602.14276/) (2026)
- [TABLET: Table Structure Recognition using Encoder-only Transformers](https://mlsanity.com/paper/2506.07015/) (2025)
- [Boosting Document Parsing Efficiency and Performance with Coarse-to-Fine Visual Processing](https://mlsanity.com/paper/2603.24326/) (2026)
- [LLM-as-a-Judge Scores Are Unreliable Optimization Signals in Closed-Loop Table Recognition](https://mlsanity.com/paper/2607.13347/) (2026)
- [ESG Accountability Made Easy: DocQA at Your Service](https://mlsanity.com/paper/2311.18481/) (2023)
- [PdfTable: A Unified Toolkit for Deep Learning-Based Table Extraction](https://mlsanity.com/paper/2409.05125/) (2024)
- [TDATR: Improving End-to-End Table Recognition via Table Detail-Aware Learning and Cell-Level Visual Alignment](https://mlsanity.com/paper/2603.22819/) (2026)
- [Q-Mask: Query-driven Causal Masks for Text Anchoring in OCR-Oriented Vision-Language Models](https://mlsanity.com/paper/2604.00161/) (2026)
- [InstructTable: Improving Table Structure Recognition Through Instructions](https://mlsanity.com/paper/2604.02880/) (2026)
- [MaterioMiner -- An ontology-based text mining dataset for extraction of process-structure-property entities](https://mlsanity.com/paper/2408.04661/) (2024)
- [CISOL: An Open and Extensible Dataset for Table Structure Recognition in the Construction Industry](https://mlsanity.com/paper/2501.15469/) (2025)
- [SepFormer: Coarse-to-fine Separator Regression Network for Table Structure Recognition](https://mlsanity.com/paper/2506.21920/) (2025)
- [Benchmark It Yourself (BIY): Preparing a Dataset and Benchmarking AI Models for Scatterplot-Related Tasks](https://mlsanity.com/paper/2510.06071/) (2025)
- [VERSE: Visual Embedding Reduction and Space Exploration. Clustering-Guided Insights for Training Data Enhancement in Visually-Rich Document Understanding](https://mlsanity.com/paper/2601.05125/) (2026)
- [TableSeq: Unified Generation of Structure, Content, and Layout](https://mlsanity.com/paper/2604.16070/) (2026)
- [LIFT: Last-Mile Fine-Tuning for Table Explicitation](https://mlsanity.com/paper/2605.13424/) (2026)
- [FastTab: A Fast Table Recognizer with a Tiny Recursive Module and 1D Transformers](https://mlsanity.com/paper/2605.22422/) (2026)
- [Rethinking the Pointer Loss in Table Structure Recognition: Geometry-Aware Pointer Loss for Spatial Locality](https://mlsanity.com/paper/2606.18721/) (2026)
- [ConRTF: Edge-Constrained Boundary Distribution Refinement for Realtime TransFormer Table Structure Recognition](https://mlsanity.com/paper/2607.00734/) (2026)
- [From Diagnosis to Correction: Benchmarking and Improving Real-World Table Parsing](https://mlsanity.com/paper/2608.09842/) (2026)

## Abstract (arXiv)

Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs.