Focus-Enhanced Scene Text Recognition with Deformable Convolutions
원문 초록
Recently, scene text recognition methods based on deep learning have sprung up in computer vision area. The existing methods achieved great performances, but the recognition of irregular text is still challenging due to the various shapes and distorted patterns. Consider that at the time of reading words in the real world, normally we will not rectify it in our mind but adjust our focus and visual fields. Similarly, through utilizing deformable convolutional layers whose geometric structures are adjustable, we present an enhanced recognition network without the steps of rectification to deal with irregular text in this work. A number of experiments have been applied, where the results on public benchmarks demonstrate the effectiveness of our proposed components and shows that our method has reached satisfactory performances. The code will be publicly available at https://github.com/Alpaca07/dtr soon.
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Linguistics-aware Masked Image Modeling for Self-supervised Scene Text Recognition
EventSTR: A Benchmark Dataset and Baselines for Event Stream based Scene Text Recognition
Ocean-OCR: Towards General OCR Application via a Vision-Language Model
TextSSR: Diffusion-based Data Synthesis for Scene Text Recognition
SVTRv2: CTC Beats Encoder-Decoder Models in Scene Text Recognition
Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing
토론
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