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arxiv 1604.06646 v1 pith:XBS6RLO2 submitted 2016-04-22 cs.CV

Synthetic Data for Text Localisation in Natural Images

Ankush Gupta , Andrea Vedaldi , Andrew Zisserman This is my paper
classification cs.CV
keywords imagestextdetectionnaturalsyntheticenginefcrnmethod
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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In this paper we introduce a new method for text detection in natural images. The method comprises two contributions: First, a fast and scalable engine to generate synthetic images of text in clutter. This engine overlays synthetic text to existing background images in a natural way, accounting for the local 3D scene geometry. Second, we use the synthetic images to train a Fully-Convolutional Regression Network (FCRN) which efficiently performs text detection and bounding-box regression at all locations and multiple scales in an image. We discuss the relation of FCRN to the recently-introduced YOLO detector, as well as other end-to-end object detection systems based on deep learning. The resulting detection network significantly out performs current methods for text detection in natural images, achieving an F-measure of 84.2% on the standard ICDAR 2013 benchmark. Furthermore, it can process 15 images per second on a GPU.

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