Abstract
Text detection is important for many applications such as text retrieval, blind guidance, and industrial automation. Meanwhile, text detection is a challenging task due to the complexity of the background and the diversity of the font, size and color of the text. In recent years, deep learning achieves good results in image classification and detection, and provides us a new method for text detection. In this paper, a deep learning based detection method – Single Shot MultiBox Detector (SSD) is adopted. But SSD is a general object detection method, not specific for text detection and is not fast enough. Our method aims to develop a network for text detection, improve the speed and reduce the model. Therefore, we design a feature extraction network with the inception module and an additional deconvolution layer. The experiment on benchmark – ICDAR2013 demonstrates that our method is faster than other SSD-based method comparable results.
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