Curved synthetic training data, aspect-ratio-preserving resizing, and rotation augmentation push rectification-based scene text recognizers to state-of-the-art accuracy on curved text benchmarks.
Scene Text Detection and Recognition: The Deep Learning Era
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abstract
With the rise and development of deep learning, computer vision has been tremendously transformed and reshaped. As an important research area in computer vision, scene text detection and recognition has been inescapably influenced by this wave of revolution, consequentially entering the era of deep learning. In recent years, the community has witnessed substantial advancements in mindset, approach and performance. This survey is aimed at summarizing and analyzing the major changes and significant progresses of scene text detection and recognition in the deep learning era. Through this article, we devote to: (1) introduce new insights and ideas; (2) highlight recent techniques and benchmarks; (3) look ahead into future trends. Specifically, we will emphasize the dramatic differences brought by deep learning and the grand challenges still remained. We expect that this review paper would serve as a reference book for researchers in this field. Related resources are also collected and compiled in our Github repository: https://github.com/Jyouhou/SceneTextPapers.
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Rethinking Irregular Scene Text Recognition
Curved synthetic training data, aspect-ratio-preserving resizing, and rotation augmentation push rectification-based scene text recognizers to state-of-the-art accuracy on curved text benchmarks.