Hailiang Xu
ABSTRACT
Text detection in natural scene images is an open and challenging problem due to the significant variations of the appearance of the text itself and its interaction with the context. In this paper, we present a novel text detection method based on robust localization and adaptive growing of seed text components. The method consists of two main ingredients. First, convolutional neural network is exploited to localize seed candidate characters from the maximally stable extremal regions of the image with learned discriminative deep convolutional features. Next, an iterative and adaptive growing algorithm is employed to grow from seed characters to search for other degraded text components in same text line based on their conformity to the seed, and an associative quality is learned to measure the conformity combining both the geometric and appearance constraints between two neighbouring text components. The effectiveness of the proposed method is demonstrated by the state-of-the-art results achieved on the public datasets.
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Index Terms
- Robust Seed Localization and Growing with Deep Convolutional Features for Scene Text Detection
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