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- W4383746192 abstract "Abstract Text, which is regarded as one of the important clues for visual recognition, can provide rich and accurate high-level semantic information. Therefore, the detection and recognition of textual data have become a research hotspot in computer vision and artificial intelligence. However, the difficulty of data collection and the non-uniform distribution of characters still poses challenges for accurate text recognition, especially for recognizing complicated character sets, such as Chinese. To address small-sample text recognition, we propose an improved image-based text transfer framework, named $$mathrm T^2$$ <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML> <mml:msup> <mml:mi>T</mml:mi> <mml:mn>2</mml:mn> </mml:msup> </mml:math> Net. This work can replace or modify the text content in an image so as to arbitrarily expand a recognition data set. Considering that the main challenge of text transfer lies in decoupling the complex interrelationship between text and background, a text content mask branch is first added into a background inpainting module so as to more realistically restore background textures. Second, a text recognition model is developed to guide the readability of the text transfer results in the text conversion module. Finally, a text fusion module is used to fuse the independent migrations of background and text. We examined the performance of our proposed framework in a real-word scene text recognition data set. Qualitative and quantitative results have proved the efficiency of our method in comparison with previous works." @default.
- W4383746192 created "2023-07-11" @default.
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- W4383746192 date "2023-07-11" @default.
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- W4383746192 title "$$mathrm T^2$$Net: an improved image-based text transfer framework using background inpainting and text conversion" @default.
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- W4383746192 doi "https://doi.org/10.1007/s44244-023-00010-6" @default.
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