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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 3

Scene text recognition based on multimodal feature fusion

Cai Mingzhe
Wang Manli
Dou Zeya
Zhang Changsen
School of Physics & Electronic Information Engineering, Henan Polytechnic University, JiaoZuo HeNan 454003, China
DOI: 10.19734/j.issn.1001-3695.2024.05.0250

Abstract

Toward addressing the challenges posed by occlusions, distortions, and other impediments in recognizing text within natural scenes, a scene text recognition network (Multimodal scene text recognition, MMSTR) based on multi-modal feature fusion was proposed. Firstly, MMSTR employs a shared-weight internal autoregressive permutation language model to facilitate a variety of decoding strategies. Secondly, during the image encoding phase, MMSTR introduces a Residual Attention Encoder (REA-Encoder) , which enhances the capability of capturing shallow features, allowing them to propagate to deeper network layers. This effectively alleviates the issue of feature collapse resulting from the inadequate extraction of shallow image features by Vision Transformers. Finally, to address the insufficient fusion of semantic and visual features during the decoding process, MMSTR constructs a Decision Fusion Module (DFM) . The DFM utilizes a cascaded multi-head attention mechanism to enhance the integration of semantic and visual features. Experimental evidence confirms that MMSTR attains an average word accuracy rate of 96.6% across six public datasets, including IIIT5K and ICDAR13. Furthermore, MMSTR exhibits a significant advantage over other mainstream algorithms in the recognition of challenging text images that are obscured or distorted.

Foundation Support

国家自然科学基金资助项目(52074305,河南省科技攻关项目(242102221006)
河南省研究生教育改革与质量提升工程(YJS2024AL026)
河南理工大学光电传感与智能测控河南省工程实验室开放基金资助项目(HELPSIMC-2020-00X)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.05.0250
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 3

Publish History

[2024-12-06] Accepted Paper

Cite This Article

蔡明哲, 王满利, 窦泽亚, 等. 基于多模态特征融合的场景文本识别 [J]. 计算机应用研究, 2025, 42 (3). (2024-12-16). https://doi.org/10.19734/j.issn.1001-3695.2024.05.0250. (Cai Mingzhe, Wang Manli, Dou Zeya, et al. Scene text recognition based on multimodal feature fusion [J]. Application Research of Computers, 2025, 42 (3). (2024-12-16). https://doi.org/10.19734/j.issn.1001-3695.2024.05.0250. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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