Detecting Text in Natural Image with Connectionist Text Proposal Network
We propose a novel Connectionist Text Proposal Network (CTPN) that accurately localizes text lines in natural image. The CTPN detects a text line in a sequence of fine scale text proposals directly in convolutional feature maps. We develop a vertical anchor mechanism that jointly predicts location and text/non text score of each fixed width proposal, considerably improving localization accuracy. The sequential proposals are naturally connected by a recurrent neural network, which is seamlessly incorporated into the convolutional network, resulting in an end to end trainable model. This allows the CTPN to explore rich context information of image, making it powerful to detect extremely ambiguous text. The CTPN works reliably on multi scale and multi language text without further post processing, departing from previous bottom up methods requiring multi step post filtering. It achieves 0.88 and 0.61 F measure on the ICDAR 2013 and 2015 benchmarks, surpassing recent results [8, 35] by a large margin. The CTPN is computationally efficient with 0.14 s/image, by using the very deep VGG16 model [27] Online demo is available: http:/textdet.com/
ECCV