Improving Text Recognition in Tor darknet with Rectification and Super-Resolution techniques
Pablo Blanco‐Medina, Eduardo Fidalgo, Enrique Alegre, Francisco Jáñez-Martino · 2019
Text recognition can be used to retrieve textual information embedded in images. This task can be complex due to the low-resolution of the images and the orientation of the text, which are problems commonly found in Tor darknet images. In this work, we combine three different super-resolution algorithms, together with a rectification network, to increase the performance in the text recognition step. We evaluated these combinations in four state-of-the-art datasets, and in TOICO-1K, a Tor-based image dataset which was semi-automatically labelled for the task of Text Spotting in Tor darknet. We obtained the highest performance increase in ICDAR 2015 dataset, with an improvement of 3.77% when combining Residual Dense and the rectification networks. In TOICO-1K, we achieved a 3.41% of improvement when we combined Deep CNN and the rectification network. Our conclusion is that rectification performs slightly better than super-resolution when they are applied standalone, while their combination obtains the best results in the datasets evaluated.