Alchemy: Techniques for Rectification Based Irregular Scene Text Recognition

by   Shangbang Long, et al.
Carnegie Mellon University
Peking University

Reading text from natural images is challenging due to the great variety in text font, color, size, complex background and etc.. The perspective distortion and non-linear spatial arrangement of characters make it further difficult. While rectification based method is intuitively grounded and has pushed the envelope by far, its potential is far from being well exploited. In this paper, we present a bag of tricks that prove to significantly improve the performance of rectification based method. On curved text dataset, our method achieves an accuracy of 89.6 previous state-of-the-art by 6.3 combination of tricks helps us win the ICDAR 2019 Arbitrary-Shaped Text Challenge (Latin script), achieving an accuracy of 74.3 set. We release our code as well as data samples for further exploration at


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