LOGO: Video Text Spotting with Language Collaboration and Glyph Perception Model

TL;DR

The paper introduces LOGO, a novel video text spotting framework that combines language collaboration and glyph perception to improve detection, recognition, and tracking of text in videos, especially under challenging conditions.

Contribution

LOGO integrates a language synergy classifier and glyph supervision into existing text spotters, enhancing low-resolution text detection and recognition accuracy without extensive fine-tuning.

Findings

Improves detection and recognition of low-resolution text instances.

Effectively filters out text-like background regions.

Achieves state-of-the-art performance on public benchmarks.

Abstract

Video text spotting (VTS) aims to simultaneously localize, recognize and track text instances in videos. To address the limited recognition capability of end-to-end methods, recent methods track the zero-shot results of state-of-the-art image text spotters directly, and achieve impressive performance. However, owing to the domain gap between different datasets, these methods usually obtain limited tracking trajectories on extreme dataset. Fine-tuning transformer-based text spotters on specific datasets could yield performance enhancements, albeit at the expense of considerable training resources. In this paper, we propose a Language Collaboration and Glyph Perception Model, termed LOGO, an innovative framework designed to enhance the performance of conventional text spotters. To achieve this goal, we design a language synergy classifier (LSC) to explicitly discern text instances from background noise in the recognition stage. Specially, the language synergy classifier can output text content or background code based on the legibility of text regions, thus computing language scores. Subsequently, fusion scores are computed by taking the average of detection scores and language scores, and are utilized to re-score the detection results before tracking. By the re-scoring mechanism, the proposed LSC facilitates the detection of low-resolution text instances while filtering out text-like regions. Moreover, the glyph supervision is introduced to enhance the recognition accuracy of noisy text regions. In addition, we propose the visual position mixture module, which can merge the position information and visual features efficiently, and acquire more discriminative tracking features. Extensive experiments on public benchmarks validate the effectiveness of the proposed method.