A Hybrid Approach for Document Layout Analysis in Document images

TL;DR

This paper introduces a hybrid Transformer-based approach for document layout analysis that improves detection accuracy of various elements in document images, outperforming existing methods on multiple benchmarks.

Contribution

It proposes a novel graphical page object detector with a query encoding mechanism and hybrid matching scheme, enhancing detection accuracy and efficiency in document layout analysis.

Findings

Achieves 97.3% AP on PubLayNet

Attains 81.6% AP on DocLayNet

Reaches 98.6% AP on PubTables

Abstract

Document layout analysis involves understanding the arrangement of elements within a document. This paper navigates the complexities of understanding various elements within document images, such as text, images, tables, and headings. The approach employs an advanced Transformer-based object detection network as an innovative graphical page object detector for identifying tables, figures, and displayed elements. We introduce a query encoding mechanism to provide high-quality object queries for contrastive learning, enhancing efficiency in the decoder phase. We also present a hybrid matching scheme that integrates the decoder's original one-to-one matching strategy with the one-to-many matching strategy during the training phase. This approach aims to improve the model's accuracy and versatility in detecting various graphical elements on a page. Our experiments on PubLayNet, DocLayNet, and PubTables benchmarks show that our approach outperforms current state-of-the-art methods. It achieves an average precision of 97.3% on PubLayNet, 81.6% on DocLayNet, and 98.6 on PubTables, demonstrating its superior performance in layout analysis. These advancements not only enhance the conversion of document images into editable and accessible formats but also streamline information retrieval and data extraction processes.