Self-Supervised Pre-training with Symmetric Superimposition Modeling for Scene Text Recognition

TL;DR

This paper introduces Symmetric Superimposition Modeling (SSM), a self-supervised pre-training method that captures both local character features and linguistic information in scene text recognition, leading to significant performance improvements.

Contribution

The novel SSM approach models both pixel-level and feature-level linguistic information using symmetric superimposition, enhancing text recognition accuracy without reliance on extensive annotations.

Findings

Achieves 4.1% average performance gain on benchmarks.

Sets new state-of-the-art with 86.6% average word accuracy.

Demonstrates effectiveness and generality across various datasets.

Abstract

In text recognition, self-supervised pre-training emerges as a good solution to reduce dependence on expansive annotated real data. Previous studies primarily focus on local visual representation by leveraging mask image modeling or sequence contrastive learning. However, they omit modeling the linguistic information in text images, which is crucial for recognizing text. To simultaneously capture local character features and linguistic information in visual space, we propose Symmetric Superimposition Modeling (SSM). The objective of SSM is to reconstruct the direction-specific pixel and feature signals from the symmetrically superimposed input. Specifically, we add the original image with its inverted views to create the symmetrically superimposed inputs. At the pixel level, we reconstruct the original and inverted images to capture character shapes and texture-level linguistic context. At the feature level, we reconstruct the feature of the same original image and inverted image with different augmentations to model the semantic-level linguistic context and the local character discrimination. In our design, we disrupt the character shape and linguistic rules. Consequently, the dual-level reconstruction facilitates understanding character shapes and linguistic information from the perspective of visual texture and feature semantics. Experiments on various text recognition benchmarks demonstrate the effectiveness and generality of SSM, with 4.1% average performance gains and 86.6% new state-of-the-art average word accuracy on Union14M benchmarks. The code is available at https://github.com/FaltingsA/SSM.