Coordinate-Aware End-to-End Document Parser: CREPE Enables Simultaneous Extraction of Parsing Outputs and Text Coordinates

Core Concepts

CREPE is a novel end-to-end architecture that can simultaneously generate parsing outputs and extract text coordinates from document images, overcoming the limitations of traditional OCR-based and OCR-free approaches.

Abstract

The paper introduces CREPE (Coordinate-aware End-to-End Document Parser), a new visual document understanding (VDU) model that can simultaneously generate parsing outputs and extract text coordinates from document images.
Key highlights:

CREPE employs a multi-head architecture that generates both parsing outputs and text coordinates, addressing the limitations of previous OCR-based and OCR-free approaches.
CREPE introduces special tokens ( and ) to associate text strings with their corresponding coordinates, enabling the model to learn text localization without explicit coordinate annotations.
The paper proposes a weakly-supervised learning framework that allows CREPE to be trained on datasets with only parsing annotations, without requiring costly text coordinate annotations.
Experiments demonstrate that CREPE achieves state-of-the-art performance on various document parsing benchmarks while also extracting text coordinates.
CREPE's capabilities are further showcased through its successful application to other VDU tasks like layout analysis and document visual question answering.
The authors also explore CREPE's adaptability to scene understanding tasks beyond document understanding, such as simultaneous scene text detection and object detection.
Overall, CREPE introduces a novel end-to-end architecture that can efficiently extract both semantic parsing outputs and text coordinates, paving the way for more comprehensive and practical document understanding solutions.

Stats

"CREPE achieved state-of-the-art performance on the CORD dataset with an F1-score of 85.0."
"On the TrainTicket dataset, CREPE's performance was notably better than Donut, achieving an F1-score of 98.4 compared to Donut's 94.1."
"In the evaluation of text localization performance via CLEval, CREPE achieved an F1-score of 95.5 on the CORD dataset and 89.4 on the POIE dataset."

Quotes

"CREPE simultaneously generates parsing output along with the coordinates of text strings using the multi-head architecture."
"Without text coordinate annotations, CREPE can be trained on a dataset with annotations only for parsing, thanks to our newly proposed weakly supervised learning framework."
"CREPE can be applied to various applications that require image coordinates such as document layout analysis tasks, not only for parsing tasks."

Key Insights Distilled From

CREPE: Coordinate-Aware End-to-End Document Parser

by Yamato Okamo... at arxiv.org 05-02-2024

https://arxiv.org/pdf/2405.00260.pdf

Deeper Inquiries

How can CREPE's multi-head architecture be further extended to handle more complex document structures, such as nested layouts or tables?

CREPE's multi-head architecture can be extended to handle more complex document structures by incorporating specialized heads for different types of elements within the document. For nested layouts, additional hierarchical parsing heads can be introduced to capture the nested structure of the document. These heads can work in conjunction with the existing sequence and coordinate heads to parse and extract information from nested elements. By incorporating attention mechanisms that can focus on different levels of hierarchy, CREPE can effectively handle nested layouts.
For tables, a dedicated table parsing head can be added to the architecture. This head can be designed to recognize table structures, extract tabular data, and infer relationships between table cells. By training the model on annotated table data, CREPE can learn to identify table boundaries, column headers, and cell contents. The table parsing head can generate structured output representing the tabular data, making it easier to extract and process information from tables in documents.
Overall, by expanding the multi-head architecture to include specialized heads for nested layouts and tables, CREPE can enhance its document parsing capabilities and effectively handle more complex document structures.

What are the potential limitations of CREPE's weakly-supervised learning approach, and how can it be improved to handle a wider range of document types and layouts?

One potential limitation of CREPE's weakly-supervised learning approach is the reliance on synthetic data for training the OCR functionality. While synthetic data can provide a large volume of training samples, it may not fully capture the variability and complexity of real-world document images. This can lead to challenges in generalizing the model to unseen data and handling diverse document types and layouts.
To address this limitation and improve the model's performance on a wider range of document types and layouts, several enhancements can be considered:

Semi-supervised Learning: Incorporating a semi-supervised learning approach where the model is trained on a combination of labeled and unlabeled data can help improve generalization to unseen document types.
Transfer Learning: Pre-training the model on a diverse set of document datasets before fine-tuning on specific tasks can enhance the model's ability to handle different layouts and structures.
Data Augmentation: Introducing data augmentation techniques specific to document images, such as rotation, scaling, and perspective transformation, can help the model learn robust features for various document layouts.
Domain-specific Training: Tailoring the weakly-supervised framework to include domain-specific constraints and features can improve the model's performance on specific document types, such as invoices, forms, or academic papers.

By incorporating these strategies, CREPE's weakly-supervised learning approach can be enhanced to handle a wider range of document types and layouts with improved accuracy and robustness.

Given CREPE's demonstrated adaptability to scene understanding tasks, how can the model's capabilities be leveraged to enable more holistic visual understanding in real-world applications beyond document processing?

CREPE's adaptability to scene understanding tasks opens up opportunities for leveraging its capabilities in various real-world applications beyond document processing. To enable more holistic visual understanding, CREPE can be applied in the following ways:

Autonomous Vehicles: By integrating CREPE into the perception system of autonomous vehicles, the model can assist in scene understanding, object detection, and text recognition for navigation and safety purposes.

Retail and E-commerce: CREPE can be used for product recognition, inventory management, and visual search applications in retail and e-commerce settings. The model can identify products, extract pricing information, and enhance the shopping experience for customers.

Healthcare: In healthcare settings, CREPE can aid in medical image analysis, patient record digitization, and document processing for improved efficiency and accuracy in healthcare operations.

Smart Cities: By deploying CREPE in smart city initiatives, the model can analyze urban scenes, monitor traffic patterns, detect anomalies, and enhance overall city management and planning.

Environmental Monitoring: CREPE's scene understanding capabilities can be utilized for environmental monitoring, such as analyzing satellite images, detecting changes in land use, and monitoring natural disasters.

Overall, by leveraging CREPE's adaptability and scene understanding capabilities, real-world applications can benefit from enhanced visual understanding, improved decision-making, and increased automation in various domains.