Artistic-style text detector and a new Movie-Poster dataset

TL;DR

This paper introduces a novel artistic-style text detection method utilizing Criss-Cross Attention and residual dense blocks, along with a new Movie-Poster dataset, to improve detection accuracy in complex artistic scenarios.

Contribution

The paper presents a new detection framework combining feature enhancement and boundary discrimination, and provides a specialized dataset for artistic-style text in movie posters.

Findings

Superior performance on the Movie-Poster dataset

Effective suppression of background noise in feature fusion

Accurate boundary proposal generation in artistic text detection

Abstract

Although current text detection algorithms demonstrate effectiveness in general scenarios, their performance declines when confronted with artistic-style text featuring complex structures. This paper proposes a method that utilizes Criss-Cross Attention and residual dense block to address the incomplete and misdiagnosis of artistic-style text detection by current algorithms. Specifically, our method mainly consists of a feature extraction backbone, a feature enhancement network, a multi-scale feature fusion module, and a boundary discrimination module. The feature enhancement network significantly enhances the model's perceptual capabilities in complex environments by fusing horizontal and vertical contextual information, allowing it to capture detailed features overlooked in artistic-style text. We incorporate residual dense block into the Feature Pyramid Network to suppress the effect of background noise during feature fusion. Aiming to omit the complex post-processing, we explore a boundary discrimination module that guides the correct generation of boundary proposals. Furthermore, given that movie poster titles often use stylized art fonts, we collected a Movie-Poster dataset to address the scarcity of artistic-style text data. Extensive experiments demonstrate that our proposed method performs superiorly on the Movie-Poster dataset and produces excellent results on multiple benchmark datasets. The code and the Movie-Poster dataset will be available at: https://github.com/biedaxiaohua/Artistic-style-text-detection