Focus-Enhanced Scene Text Recognition with Deformable Convolutions

August 29, 2019 · Entered Twilight · 🏛 2019 IEEE 5th International Conference on Computer and Communications (ICCC)

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Repo contents: README.md, dataset.py, eval.py, models, test_data, torch_deform_conv, train.py, utils.py

Authors Linjie Deng, Yanxiang Gong, Xinchen Lu, Xin Yi, Zheng Ma, Mei Xie arXiv ID 1908.10998 Category cs.CV: Computer Vision Citations 15 Venue 2019 IEEE 5th International Conference on Computer and Communications (ICCC) Repository https://github.com/Alpaca07/dtr ⭐ 26 Last Checked 1 month ago

Abstract

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.