ORIGINAL RESEARCH article
Front. Big Data
Sec. Data Mining and Management
This article is part of the Research TopicMachine Learning for Large-Scale Data Processing: Algorithms and ApplicationsView all articles
Deep Learning-Enabled Hybrid Systems for Accurate Recognition of Text in Seal Images
Provisionally accepted
- 1Chongqing Polytechnic University of Electronic Technology, Chongqing, China
- 2Chongqing Three Gorges University, Chongqing, China
- 3Chongqing University, Chongqing, China
- 4Chongqing Ant Consumer Finance Co, chongqing, China
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Chinese seals are widely used in various fields within Chinese society as a tool for certifying legal documents. However, recognizing text on these seals presents challenges due to background text, high noise levels, and minimalistic image features. This paper introduces a hybrid model to address these difficulties in Chinese seal text recognition. Our model integrates preprocessing techniques tailored for real seals, a deep learning-based position correction model, a circular text unwrapping model, and OCR text recognition. First, we apply a color-based method to effectively remove the black background text on seals, eliminating redundant information while retaining crucial features for further analysis. Next, we introduce an innovative image denoising algorithm to significantly improve the system's robustness in processing noisy seal images. Additionally, we develop a deep learning-based angle prediction network and create synthetic datasets that mimic real seal scenes, enabling optimal seal image positioning for enhanced text flattening and recognition, thus boosting overall system performance. Finally, polar coordinate transformation is employed to convert the circular seal into a rectangular image for more efficient text recognition. Experimental results indicate that our proposed methods effectively enhance the accuracy of seal text recognition.
Keywords: deep learning, image denoising, optimization algorithm, Seal text recognition, Text recognition
Received: 25 Nov 2025; Accepted: 17 Dec 2025.
Copyright: © 2025 Zhang, Guan, Wu, Li, Lü, Liu, Wang, Wang and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Mingyu Guan
Qingguo Lü
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