Éclair -- Extracting Content and Layout with Integrated Reading Order for Documents (2502.04223v1)

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 LLMs and Vision LLMs (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.

• The paper introduces E, a novel OCR system that extracts formatted text, spatial layouts, and semantic classifications while preserving reading order.
• E employs a ViT-like encoder and autoregressive decoder to flexibly process diverse document layouts with various prompt combinations.
• Experimental results on DROBS and DocLayNet demonstrate E's superior accuracy and robustness compared to current state-of-the-art models.

An Analytical Overview of "E: Extracting Content and Layout with Integrated Reading Order for Documents"

The paper "E: Extracting Content and Layout with Integrated Reading Order for Documents" presents a significant advancement in the field of document understanding and Optical Character Recognition (OCR). The proposed system, E, is designed as a general-purpose text-extraction tool capable of processing diverse and complex document types through an integrated reading order. It achieves state-of-the-art performance on the new benchmark DROBS, a testament to its effectiveness in handling document layout intricacies.

Key Contributions

1. Comprehensive Text Extraction: E distinguishes itself by extracting formatted text alongside bounding boxes and their semantic classes, a capability absent in many existing models. This multifunctionality is fundamental in parsing complex documents that feature intricate layouts, such as those with multiple columns, tables, and images. By presenting a novel dataset, arXiv-5M, with exhaustive annotation types, E bridges the gap between structured text, spatial information, and semantic classification.
2. Reading Order Understanding: A notable feature of E is its ability to comprehend and retain the reading order of document elements. This aspect is crucial for ensuring that extracted content maintains the intended logical flow, essential for downstream tasks like document-based question answering and data curation.
3. Innovative Data Generation and Benchmarking: The introduction of the DROBS benchmark offers a meticulously human-annotated standard for evaluating document-level OCR and semantic classification. E achieves superior accuracy on DROBS, setting a new standard for evaluation in the document understanding field.
4. Architecture and Model Efficiency: E utilizes a ViT-like encoder combined with an autoregressive decoder. This architecture is reminiscent of Donut, yet it expands its utility through the ability to handle various prompt combinations, resulting in flexible and efficient processing of different document layouts.

Experimental Results and Implications

The paper articulates E's performance across several benchmarks, demonstrating its robustness and versatility. It achieves superior reading order accuracy compared to recent models like Kosmos-2.5 and GOT across different document types. Furthermore, on the DocLayNet benchmark, E shows competitive performance in document object detection, rivaling specialized object detectors through the introduction of a methodologically unique evaluation strategy.

Implications for Future Research

The introduction of E and its benchmark repository DROBS opens multiple avenues for further research:

• Enhanced Pre-training Datasets: The development of comprehensive datasets for pre-training is crucial. The integration of structured annotations into models like E could streamline the design of even more advanced models capable of understanding complex document formats without needing extensive additional training.
• Scaling and Efficiency: The multi-token inference technique introduced in this work could be further refined to improve scaling efficiency, which is particularly beneficial for real-time applications and large-scale document processing tasks.
• End-to-End Understanding: E's architecture could inspire further research in unifying OCR with higher-level task processing, providing an architectural template for integrating multimodal learning in document understanding tasks.

In conclusion, this paper provides a substantial contribution to the field of document understanding, proposing a model that efficiently extracts and organizes content from complex document layouts. E stands as a powerful tool for the OCR community and an invaluable asset for enhancing the training data quality for LLMs, pushing the boundaries of automated document interpretation. Future research can build on this foundation to address lingering challenges and further enhance the capabilities of OCR technologies.