KVP10k: A Large-Scale Dataset for Key-Value Pair Extraction from Diverse Business Documents

Concepts de base

KVP10k is a comprehensive dataset designed to advance the field of key-value pair extraction from complex business documents, providing a large-scale, diverse, and richly annotated resource to support the development of robust information extraction models.

Résumé

The KVP10k dataset is a significant contribution to the field of document information extraction, addressing the critical need for a comprehensive and diverse dataset tailored specifically for key-value pair (KVP) extraction. The dataset includes 10,707 richly annotated pages from a wide range of business document sources, including invoices, contracts, reports, and more.

Key highlights of the dataset:

1. Diverse Sources: The dataset covers a broad spectrum of document types and sources, including web crawl data and documents from publicfiles.fcc.gov, ensuring a diverse representation of real-world business documents.

2. Detailed Annotations: The dataset features extensive annotations, including the labeling of text as keys or values, as well as the identification of unkeyed values and unvalued keys, providing a comprehensive foundation for training and evaluating KVP extraction models.

3. Benchmark and Metrics: The authors have developed a comprehensive benchmark framework with two distinct tasks - Entity Recognition and Key-Value Pair Detection - along with corresponding evaluation metrics to facilitate the assessment and comparison of KVP extraction models.

4. Baseline Results: The authors have provided initial baseline results using an LMDX-like approach, establishing a foundation for future research and advancements in this field.

The KVP10k dataset aims to address the notable gap in the availability of high-quality, diverse datasets for KVP extraction, which has hindered the progress of document understanding technologies. By providing this resource, the authors hope to catalyze further research and innovation in the domain of information extraction from complex business documents, ultimately benefiting a wide range of industries and organizations.

Stats

The dataset contains 10,707 richly annotated pages from diverse business document sources.
The dataset includes a broad range of document types, such as invoices, contracts, reports, and more.
The annotations cover key-value pairs, unkeyed values, and unvalued keys, providing a comprehensive representation of the information within the documents.

Citations

"KVP10k sets itself apart with its extensive diversity in data and richly detailed annotations, paving the way for advancements in the field of information extraction from complex business documents."
"The significance of our contribution lies not just in the dataset itself but also in the potential it unlocks for future research and applications."

Idées clés tirées de

KVP10k : A Comprehensive Dataset for Key-Value Pair Extraction in Business Documents

by Oshri Napars... à arxiv.org 05-02-2024

https://arxiv.org/pdf/2405.00505.pdf

Questions plus approfondies

How can the KVP10k dataset be leveraged to develop models that can handle the extraction of key-value pairs from documents with varying degrees of complexity and structure?

The KVP10k dataset provides a rich and diverse set of annotated images that can serve as a valuable resource for training and evaluating models for key-value pair extraction. To leverage this dataset effectively, researchers and developers can employ several strategies:

Training Deep Learning Models: The annotated images in KVP10k can be used to train deep learning models, such as convolutional neural networks (CNNs) and transformer-based models, to extract key-value pairs from documents. By feeding the images and corresponding annotations into these models, they can learn to identify and link key and value entities accurately.

Fine-tuning Pre-trained Models: Pre-trained language models like BERT or RoBERTa can be fine-tuned on the KVP10k dataset to adapt them to the specific task of key-value pair extraction. Fine-tuning allows the models to learn the nuances of document structures and relationships between key and value entities.

Data Augmentation: To handle varying degrees of complexity and structure in documents, data augmentation techniques can be applied to the KVP10k dataset. This can involve introducing noise, rotation, or other transformations to the images to make the models more robust and adaptable to different document layouts.

Ensemble Learning: Combining multiple models trained on different subsets of the KVP10k dataset can enhance the overall performance and generalization capabilities. Ensemble learning techniques can help mitigate the limitations of individual models and improve the overall extraction accuracy.

Continuous Evaluation and Improvement: Regularly evaluating model performance on the KVP10k benchmark and iteratively refining the models based on feedback can lead to continuous improvement. This iterative process helps in developing models that can handle a wide range of document complexities effectively.

By employing these strategies, developers can harness the diversity and richness of the KVP10k dataset to build robust and accurate models for key-value pair extraction from documents.

What are the potential challenges and limitations in applying the KVP10k benchmark to real-world business scenarios, and how can the dataset be further expanded or refined to address these challenges?

While the KVP10k dataset offers a comprehensive resource for key-value pair extraction, there are several challenges and limitations that need to be considered when applying the benchmark to real-world business scenarios:

Generalization to Unseen Data: One challenge is the generalization of models trained on the KVP10k dataset to unseen data in real-world business documents. The dataset may not fully capture the diversity of document types and layouts encountered in practical applications, leading to potential performance degradation on unfamiliar document structures.

Scalability and Efficiency: Real-world business scenarios often involve large volumes of documents that require efficient and scalable extraction methods. The KVP10k dataset may need to be expanded to include a more extensive collection of documents to ensure the scalability and efficiency of models trained on the dataset.

Domain-specific Adaptation: Business documents can vary significantly across different industries and domains, requiring models to be adaptable to specific terminologies and formats. The KVP10k dataset may need to be refined with domain-specific annotations and data to enhance the models' performance in specialized business contexts.

Handling Noisy Data: Real-world documents may contain noise, errors, or inconsistencies that can impact the accuracy of key-value pair extraction. The dataset could be expanded with noisy or imperfect annotations to help models learn to handle such challenges effectively.

To address these challenges and limitations, the KVP10k dataset can be further expanded or refined in the following ways:

Inclusion of More Diverse Document Types: Adding a broader range of document types, such as legal contracts, medical records, or financial reports, can enhance the dataset's applicability to various business scenarios.

Domain-specific Annotations: Incorporating domain-specific annotations and data from different industries can help tailor the dataset to specific business contexts, improving model performance in specialized domains.

Noisy Data Generation: Introducing synthetic noise or errors into the dataset can help models learn to handle real-world data imperfections, making them more robust in practical applications.
By addressing these challenges and expanding the dataset in a targeted manner, the KVP10k benchmark can be better equipped to meet the demands of real-world business scenarios and facilitate the development of more effective key-value pair extraction models.

Given the diverse nature of the documents in KVP10k, how can the dataset be utilized to explore the intersection of document understanding, natural language processing, and computer vision, and what insights might emerge from such cross-disciplinary research?

The diverse nature of the documents in the KVP10k dataset presents a unique opportunity to explore the intersection of document understanding, natural language processing (NLP), and computer vision. By leveraging the dataset in a cross-disciplinary manner, researchers can uncover valuable insights and advancements in the following ways:

Document Structure Analysis: The dataset can be used to study how different document layouts and structures impact the performance of document understanding models. By combining computer vision techniques for layout analysis with NLP methods for text extraction, researchers can gain a deeper understanding of how documents are organized and how information is presented.

Semantic Understanding: Through the annotation of key-value pairs, the dataset enables researchers to delve into the semantic relationships between different elements in documents. By applying NLP techniques for semantic analysis and entity linking, insights can be gained into how key and value entities are related and how they contribute to the overall document meaning.

Multi-modal Fusion: The combination of text and visual information in the dataset allows for the exploration of multi-modal fusion techniques. By integrating information from both modalities, researchers can develop models that leverage the strengths of both computer vision and NLP for more comprehensive document understanding.

Cross-domain Transfer Learning: The diverse range of document types in the dataset provides an opportunity for cross-domain transfer learning. By training models on documents from various domains, insights can be gained into how knowledge learned from one domain can be transferred to improve performance in another domain.

Insights that might emerge from such cross-disciplinary research include enhanced document understanding capabilities, improved information extraction accuracy, and the development of more robust and adaptable models for handling complex business documents. By bridging the gap between document understanding, NLP, and computer vision, researchers can unlock new possibilities for information extraction and document analysis in diverse real-world applications.