Language Matters: A Weakly Supervised Vision-Language Pre-training Approach for Scene Text Detection and Spotting

TL;DR

This paper introduces oCLIP, a weakly supervised vision-language pre-training approach that enhances scene text detection and spotting by jointly learning visual and textual features, even with limited annotations.

Contribution

The paper presents a novel weakly supervised pre-training method for scene text tasks, enabling effective learning from partial annotations and improving downstream performance.

Findings

Improves F-score by +2.5% and +4.8% on detection and spotting tasks.

Outperforms existing pre-training methods on multiple datasets.

Enables learning from weakly annotated images without bounding boxes.

Abstract

Recently, Vision-Language Pre-training (VLP) techniques have greatly benefited various vision-language tasks by jointly learning visual and textual representations, which intuitively helps in Optical Character Recognition (OCR) tasks due to the rich visual and textual information in scene text images. However, these methods cannot well cope with OCR tasks because of the difficulty in both instance-level text encoding and image-text pair acquisition (i.e. images and captured texts in them). This paper presents a weakly supervised pre-training method, oCLIP, which can acquire effective scene text representations by jointly learning and aligning visual and textual information. Our network consists of an image encoder and a character-aware text encoder that extract visual and textual features, respectively, as well as a visual-textual decoder that models the interaction among textual and visual features for learning effective scene text representations. With the learning of textual features, the pre-trained model can attend texts in images well with character awareness. Besides, these designs enable the learning from weakly annotated texts (i.e. partial texts in images without text bounding boxes) which mitigates the data annotation constraint greatly. Experiments over the weakly annotated images in ICDAR2019-LSVT show that our pre-trained model improves F-score by +2.5\% and +4.8\% while transferring its weights to other text detection and spotting networks, respectively. In addition, the proposed method outperforms existing pre-training techniques consistently across multiple public datasets (e.g., +3.2\% and +1.3\% for Total-Text and CTW1500).