LATTE: Improving Latex Recognition for Tables and Formulae with Iterative Refinement

• URL: http://arxiv.org/abs/2409.14201v1
• Date: Sat, 21 Sep 2024 17:18:49 GMT
• Title: LATTE: Improving Latex Recognition for Tables and Formulae with Iterative Refinement
• Authors: Nan Jiang, Shanchao Liang, Chengxiao Wang, Jiannan Wang, Lin Tan,
• Abstract summary: LATTE improves the source extraction accuracy of both formulae and tables, outperforming existing techniques as well as GPT-4V. This paper proposes LATTE, the first iterative refinement framework for recognition.
• Score: 11.931911831112357
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Portable Document Format (PDF) files are dominantly used for storing and disseminating scientific research, legal documents, and tax information. LaTeX is a popular application for creating PDF documents. Despite its advantages, LaTeX is not WYSWYG -- what you see is what you get, i.e., the LaTeX source and rendered PDF images look drastically different, especially for formulae and tables. This gap makes it hard to modify or export LaTeX sources for formulae and tables from PDF images, and existing work is still limited. First, prior work generates LaTeX sources in a single iteration and struggles with complex LaTeX formulae. Second, existing work mainly recognizes and extracts LaTeX sources for formulae; and is incapable or ineffective for tables. This paper proposes LATTE, the first iterative refinement framework for LaTeX recognition. Specifically, we propose delta-view as feedback, which compares and pinpoints the differences between a pair of rendered images of the extracted LaTeX source and the expected correct image. Such delta-view feedback enables our fault localization model to localize the faulty parts of the incorrect recognition more accurately and enables our LaTeX refinement model to repair the incorrect extraction more accurately. LATTE improves the LaTeX source extraction accuracy of both LaTeX formulae and tables, outperforming existing techniques as well as GPT-4V by at least 7.07% of exact match, with a success refinement rate of 46.08% (formula) and 25.51% (table).

Related papers

• PDF-WuKong: A Large Multimodal Model for Efficient Long PDF Reading with End-to-End Sparse Sampling [63.93112754821312]
  Document understanding is a challenging task to process and comprehend large amounts of textual and visual information. Recent advances in Large Language Models (LLMs) have significantly improved the performance of this task. We introduce PDF-WuKong, a multimodal large language model (MLLM) which is designed to enhance multimodal question-answering (QA) for long PDF documents.
  arXiv  Detail & Related papers  (2024-10-08T12:17:42Z)
• TeXBLEU: Automatic Metric for Evaluate LaTeX Format [4.337656290539519]
  We propose BLEU, a metric for evaluating mathematical expressions in the format built on the n-gram-based BLEU metric. The proposed BLEU consists of a tokenizer trained on the arXiv paper dataset and a fine-tuned embedding model with positional encoding.
  arXiv  Detail & Related papers  (2024-09-10T16:54:32Z)
• MathBridge: A Large Corpus Dataset for Translating Spoken Mathematical Expressions into $LaTeX$ Formulas for Improved Readability [10.757551947236879]
  We introduce MathBridge, the first extensive dataset for translating mathematical spoken sentences into formulas. MathBridge significantly enhances the capabilities of pretrained language models for converting to formulas from mathematical spoken sentences.
  arXiv  Detail & Related papers  (2024-08-07T18:07:15Z)
• CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs [62.84082370758761]
  CharXiv is a comprehensive evaluation suite involving 2,323 charts from arXiv papers. To ensure quality, all charts and questions are handpicked, curated, and verified by human experts. Results reveal a substantial, previously underestimated gap between the reasoning skills of the strongest proprietary model.
  arXiv  Detail & Related papers  (2024-06-26T17:50:11Z)
• MathNet: A Data-Centric Approach for Printed Mathematical Expression Recognition [2.325171167252542]
  We present an improved version of the benchmark dataset im2latex-100k, featuring 30 fonts instead of one. Second, we introduce the real-world dataset realFormula, with MEs extracted from papers. Third, we developed a MER model, MathNet, based on a convolutional vision transformer, with superior results on all four test sets.
  arXiv  Detail & Related papers  (2024-04-21T14:03:34Z)
• Visually Guided Generative Text-Layout Pre-training for Document Intelligence [51.09853181377696]
  We propose visually guided generative text-pre-training, named ViTLP. Given a document image, the model optimize hierarchical language and layout modeling objectives to generate the interleaved text and layout sequence. ViTLP can function as a native OCR model to localize and recognize texts of document images.
  arXiv  Detail & Related papers  (2024-03-25T08:00:43Z)
• Doc2SoarGraph: Discrete Reasoning over Visually-Rich Table-Text Documents via Semantic-Oriented Hierarchical Graphs [79.0426838808629]
  We propose TAT-DQA, i.e. to answer the question over a visually-rich table-text document. Specifically, we propose a novel Doc2SoarGraph framework with enhanced discrete reasoning capability. We conduct extensive experiments on TAT-DQA dataset, and the results show that our proposed framework outperforms the best baseline model by 17.73% and 16.91% in terms of Exact Match (EM) and F1 score respectively on the test set.
  arXiv  Detail & Related papers  (2023-05-03T07:30:32Z)
• Mixed-modality Representation Learning and Pre-training for Joint Table-and-Text Retrieval in OpenQA [85.17249272519626]
  An optimized OpenQA Table-Text Retriever (OTTeR) is proposed. We conduct retrieval-centric mixed-modality synthetic pre-training. OTTeR substantially improves the performance of table-and-text retrieval on the OTT-QA dataset.
  arXiv  Detail & Related papers  (2022-10-11T07:04:39Z)
• SpreadsheetCoder: Formula Prediction from Semi-structured Context [70.41579328458116]
  We propose a BERT-based model architecture to represent the tabular context in both row-based and column-based formats. We train our model on a large dataset of spreadsheets, and demonstrate that SpreadsheetCoder achieves top-1 prediction accuracy of 42.51%. Compared to the rule-based system, SpreadsheetCoder 82% assists more users in composing formulas on Google Sheets.
  arXiv  Detail & Related papers  (2021-06-26T11:26:27Z)
• ICDAR 2021 Competition on Scientific Table Image Recognition to LaTeX [1.149654395906819]
  This paper discusses the dataset, tasks, participants' methods, and results of the ICDAR 2021 Competition on Scientific Table Image Recognition. We propose two subtasks: reconstruct the structure code from an image, and reconstruct the content code from an image. This report describes the datasets and ground truth specification, details the performance evaluation metrics used, presents the final results, and summarizes the participating methods.
  arXiv  Detail & Related papers  (2021-05-30T04:17:55Z)
• Reproducible Science with LaTeX [4.09920839425892]
  This paper proposes a procedure to execute external source codes from a document. It includes the calculation outputs in the resulting Portable Document Format (pdf) file automatically.
  arXiv  Detail & Related papers  (2020-10-04T04:04:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.