Shivakumara, Palaiahnakote and Roy, Ayush and Nandanwar, Lokesh and Pal, Umapada and Lu, Yue and Liu, Cheng-Lin (2023) A new lightweight script independent scene text style transfer network. International Journal of Pattern Recognition and Artificial Intelligence, 37 (13). ISSN 0218-0014, DOI https://doi.org/10.1142/S0218001423530038.
Full text not available from this repository.Abstract
Scene text style transfer without a language barrier is an open challenge for the video and scene text recognition community because this plays a vital role in poster, web design, augmenting character images, and editing characters to improve scene text recognition performance and usability. This work presents a new model, called Script Independent Scene Text Style Transfer Network (SISTSTNet), for extracting scene characters and transferring text style simultaneously. The SISTSTNet performs mapping in language-independent feature space for transferring style. It is designed based on a Style Parameter Network and Target Encoder Network through lightweight MobileNetv3 convolutional and residual blocks to capture the style and shape to generate target characters. Similarly, a generative model is explored through the Visual Geometry Group (VGG) network for character replacement. The SISTSTNet is flexible and works on different languages and arbitrary examples in a neat and unified fashion. The experimental results on images in various languages, namely, English, Chinese, Hindi, Russian, Japanese, Arabic, Greek, and Bengali and cross-language validation demonstrate the effectiveness of the proposed method. The performance of the method is superior compared to the state-of-the-art methods in terms of quality measures, language independence, shape-preserving, and efficiency. The code and dataset will be released to the public to support reproducibility.
| Item Type: | Article |
|---|---|
| Funders: | Ministry of Education, Malaysia, Fundamental Research Grant Scheme (FRGS) [Grant no. FRGS/1/2020/ICT02/UM/02/4], National Natural Science Foundation of China (NSFC) [Grant no. 62136001], Technology Innovation Hub, Indian Statistical Institute, Kolkata, India |
| Uncontrolled Keywords: | Text detection; Style transfer; CNN models; Multi-lingual transfer |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 20 Oct 2025 13:11 |
| Last Modified: | 20 Oct 2025 13:11 |
| URI: | http://eprints.um.edu.my/id/eprint/48099 |
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