Text Grouping Adapter: Adapting Pre-trained Text Detector for Layout Analysis

TL;DR

This paper introduces Text Grouping Adapter (TGA), a module that enables pre-trained text detectors to perform layout analysis by grouping text instances into paragraphs, improving efficiency and performance.

Contribution

The paper proposes TGA, a universal adapter that leverages pre-trained text detectors for layout analysis, allowing efficient fine-tuning and better utilization of existing detection datasets.

Findings

TGA improves layout analysis performance with frozen pre-trained models.

Incorporating TGA into various detectors enhances text grouping accuracy.

Fine-tuning TGA further boosts layout analysis results.

Abstract

Significant progress has been made in scene text detection models since the rise of deep learning, but scene text layout analysis, which aims to group detected text instances as paragraphs, has not kept pace. Previous works either treated text detection and grouping using separate models, or train a model from scratch while using a unified one. All of them have not yet made full use of the already well-trained text detectors and easily obtainable detection datasets. In this paper, we present Text Grouping Adapter (TGA), a module that can enable the utilization of various pre-trained text detectors to learn layout analysis, allowing us to adopt a well-trained text detector right off the shelf or just fine-tune it efficiently. Designed to be compatible with various text detector architectures, TGA takes detected text regions and image features as universal inputs to assemble text instance features. To capture broader contextual information for layout analysis, we propose to predict text group masks from text instance features by one-to-many assignment. Our comprehensive experiments demonstrate that, even with frozen pre-trained models, incorporating our TGA into various pre-trained text detectors and text spotters can achieve superior layout analysis performance, simultaneously inheriting generalized text detection ability from pre-training. In the case of full parameter fine-tuning, we can further improve layout analysis performance.