A new lightweight script independent scene text style transfer network
Document Type
Article
Publication Date
10-1-2023
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.
Keywords
Text detection, Style transfer, CNN models, Multi-lingual transfer
Publication Title
International Journal of Pattern Recognition and Artificial Intelligence
Recommended Citation
Shivakumara, Palaiahnakote; Roy, Ayush; Nandanwar, Lokesh; Pal, Umapada; Lu, Yue; and Liu, Cheng-Lin, "A new lightweight script independent scene text style transfer network" (2023). Research Publications (2021 to 2025). 3541.
https://knova.um.edu.my/research_publications_2021_2025/3541
Divisions
fsktm
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
Volume
37
Issue
13
Publisher
World Scientific Publ Co Pte Ltd
Publisher Location
5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE