Éclair -- Extracting Content and Layout with Integrated Reading Order for Documents
Ilia Karmanov, Amala Sanjay Deshmukh, Lukas Voegtle, Philipp Fischer, Kateryna Chumachenko, Timo Roman, Jarno Seppänen, Jupinder Parmar, Joseph Jennings, Andrew Tao, Karan Sapra
2025-02-12
Summary
This paper talks about Éclair, a new tool that helps computers understand complex documents better by not just reading the text but also analyzing the layout and structure, like tables, columns, and the order in which the text should be read.
What's the problem?
Current Optical Character Recognition (OCR) tools can extract text from images of documents, but they often struggle with complicated layouts or understanding the order in which text should be read. This makes it hard for these tools to work well with documents like magazines, academic papers, or forms that have multiple sections and visual elements.
What's the solution?
The researchers developed Éclair, which uses advanced AI to analyze both the visual layout and the logical reading order of a document. It can identify different parts of a document, like footnotes or captions, and extract text in a way that keeps its structure intact. They also created a new benchmark to test how well Éclair works compared to other tools, and it achieved state-of-the-art results.
Why it matters?
This matters because Éclair can make it easier to digitize and analyze complex documents accurately. This could improve tasks like searching through large collections of documents, answering questions about their content, or preparing data for training other AI models. It opens up possibilities for better handling of diverse document types in fields like research, business, and education.
Abstract
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.