title: Éclair -- Extracting Content and Layout with Integrated Reading Order
    for Documents
summary: Optical Character Recognition (OCR) technology is widely used to extract text
  from images of documents, facilitating efficient digitization and data
  retrieval. However, merely extracting text is insufficient when dealing with
  complex documents. Fully comprehending such documents requires an understanding
  of their structure -- including formatting, formulas, tables, and the reading
  order of multiple blocks and columns across multiple pages -- as well as
  semantic information for detecting elements like footnotes and image captions.
  This comprehensive understanding is crucial for downstream tasks such as
  retrieval, document question answering, and data curation for training Large
  Language Models (LLMs) and Vision Language Models (VLMs). To address this, we
  introduce \'Eclair, a general-purpose text-extraction tool specifically
  designed to process a wide range of document types. Given an image, \'Eclair is
  able to extract formatted text in reading order, along with bounding boxes and
  their corresponding semantic classes. To thoroughly evaluate these novel
  capabilities, we introduce our diverse human-annotated benchmark for
  document-level OCR and semantic classification. \'Eclair achieves
  state-of-the-art accuracy on this benchmark, outperforming other methods across
  key metrics. Additionally, we evaluate \'Eclair on established benchmarks,
  demonstrating its versatility and strength across several evaluation standards.
paper_authors._id: 67ac5e0d653d273eeaf25e59
paper_authors.name: Ilia Karmanov
paper_authors.status: claimed_verified
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paper_authors._id: 630602e8660f01f1509f791d
paper_authors.avatarUrl: /avatars/23522c79b471010cfd969d2c34675b34.svg
paper_authors.fullname: Ilia Karmanov
paper_authors.type: user
paper_authors.user: iliauk
paper_authors._id: 67ac5e0d653d273eeaf25e5a
paper_authors.name: Amala Sanjay Deshmukh
paper_authors.status: claimed_verified
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paper_authors._id: 67ac5d85a19e34140ea1013b
paper_authors.avatarUrl: /avatars/e5b7446787dbbd17553dc9e11b58a0b4.svg
paper_authors.fullname: Amala Sanjay Deshmukh
paper_authors.type: user
paper_authors.user: amalad
paper_authors._id: 67ac5e0d653d273eeaf25e5b
paper_authors.name: Lukas Voegtle
paper_authors._id: 67ac5e0d653d273eeaf25e5c
paper_authors.name: Philipp Fischer
paper_authors._id: 67ac5e0d653d273eeaf25e5d
paper_authors.name: Kateryna Chumachenko
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-12T09:14:47.025Z
paper_authors._id: 64c7a43e0d3d1b209df90b9c
paper_authors.avatarUrl: /avatars/1d0d2f129b799a72345b17fd5307aa5e.svg
paper_authors.fullname: Kateryna Chumachenko
paper_authors.type: user
paper_authors.user: katerynaCh
paper_authors._id: 67ac5e0d653d273eeaf25e5e
paper_authors.name: Timo Roman
paper_authors._id: 67ac5e0d653d273eeaf25e5f
paper_authors.name: Jarno Seppänen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-12T09:14:51.062Z
paper_authors._id: 60098ca06e8ac78787773f85
paper_authors.avatarUrl: /avatars/be6539e5706bf07c71e553254c1751b5.svg
paper_authors.fullname: Jarno Seppänen
paper_authors.type: user
paper_authors.user: jseppanen
paper_authors._id: 67ac5e0d653d273eeaf25e60
paper_authors.name: Jupinder Parmar
paper_authors._id: 67ac5e0d653d273eeaf25e61
paper_authors.name: Joseph Jennings
paper_authors._id: 67ac5e0d653d273eeaf25e62
paper_authors.name: Andrew Tao
paper_authors._id: 67ac5e0d653d273eeaf25e63
paper_authors.name: Karan Sapra
paper_title: Éclair -- Extracting Content and Layout with Integrated Reading Order
    for Documents
paper_summary: Optical Character Recognition (OCR) technology is widely used to extract text
  from images of documents, facilitating efficient digitization and data
  retrieval. However, merely extracting text is insufficient when dealing with
  complex documents. Fully comprehending such documents requires an understanding
  of their structure -- including formatting, formulas, tables, and the reading
  order of multiple blocks and columns across multiple pages -- as well as
  semantic information for detecting elements like footnotes and image captions.
  This comprehensive understanding is crucial for downstream tasks such as
  retrieval, document question answering, and data curation for training Large
  Language Models (LLMs) and Vision Language Models (VLMs). To address this, we
  introduce \'Eclair, a general-purpose text-extraction tool specifically
  designed to process a wide range of document types. Given an image, \'Eclair is
  able to extract formatted text in reading order, along with bounding boxes and
  their corresponding semantic classes. To thoroughly evaluate these novel
  capabilities, we introduce our diverse human-annotated benchmark for
  document-level OCR and semantic classification. \'Eclair achieves
  state-of-the-art accuracy on this benchmark, outperforming other methods across
  key metrics. Additionally, we evaluate \'Eclair on established benchmarks,
  demonstrating its versatility and strength across several evaluation standards.
paper_ai_keywords: Eclair text-extraction tool reading order bounding boxes semantic classes document-level OCR semantic classification Large Language Models (LLMs) Vision Language Models (VLMs)