Éclair -- Extracting Content and Layout with Integrated Reading Order
  for Documents
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.
67ac5e0d653d273eeaf25e59
Ilia Karmanov
claimed_verified
2025-02-13T08:25:14.762Z
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Ilia Karmanov
user
iliauk
67ac5e0d653d273eeaf25e5a
Amala Sanjay Deshmukh
claimed_verified
2025-02-12T09:14:49.009Z
67ac5d85a19e34140ea1013b
/avatars/87f805f9b229bfa87b3930bf8beba51b.svg
Amala Sanjay Deshmukh
user
amalad
67ac5e0d653d273eeaf25e5b
Lukas Voegtle
67ac5e0d653d273eeaf25e5c
Philipp Fischer
67ac5e0d653d273eeaf25e5d
Kateryna Chumachenko
claimed_verified
2025-07-08T08:08:16.893Z
686c1900d548ccbe024e69ca
/avatars/1230e630b1cbe03c5b00ca607128574e.svg
Kateryna Chumachenko
user
katerynaCh
67ac5e0d653d273eeaf25e5e
Timo Roman
67ac5e0d653d273eeaf25e5f
Jarno Seppänen
claimed_verified
2025-02-12T09:14:51.062Z
60098ca06e8ac78787773f85
/avatars/f13b180e7a5b2b35f188b5fb5bcf14fa.svg
Jarno Seppänen
user
jseppanen
67ac5e0d653d273eeaf25e60
Jupinder Parmar
67ac5e0d653d273eeaf25e61
Joseph Jennings
67ac5e0d653d273eeaf25e62
Andrew Tao
67ac5e0d653d273eeaf25e63
Karan Sapra
Éclair -- Extracting Content and Layout with Integrated Reading Order
  for Documents
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.
Eclair is a general-purpose OCR tool designed to extract formatted text with semantic information for document-level understanding, achieving state-of-the-art accuracy on diverse benchmarks.
optical character recognition OCR document structure formatted text reading order bounding boxes semantic classes document-level OCR semantic classification Large Language Models Vision Language Models