Text Detection in Natural Scene Images with Text Line Construction

This paper implements a new text region recognition algorithm that can accurately localize image text regions in natural image with complex background. The method is mainly based on the anchor mechanism of the faster R-CNN, taking into account the special features of the text area relative to other object detect tasks, so as to convert the text area detection task in the image into a general object detection task for the small area text. In this way, we can detect the text proposal directly in the convolutional feature map of the neural network, and it can simultaneously predict the text/non- text score of the proposal and the coordinates of each proposal in the image. Then we propose a text line construction algorithm that can combine the text regions into complete text line blocks, thus greatly improving the accuracy and reliability of our text detection model. Our text detector also works accurately in multi-scale and multi-lingual text detection tasks. It achieves 0.86 F-measure and 0.78 F-measure on the ICDAR 2011 and ICDAR 2013 benchmarks, which also confirms the accuracy of our model.