We are happy to announce that the following paper has been accepted for publication in the International Journal of Document Analysis and Recognition (IJDAR): TextCatcher: a method to detect curved and challenging text in natural scenes Jonathan Fabrizio (1) Myriam Robert-Seidowsky (1) Séverine Dubuisson (2) Stefania Calarasanu (1) Raphaël Boissel (1) (1) EPITA Research and Development Laboratory (LRDE) (2) Sorbonne Universités, UPMC - ISIR In this paper, we propose a text detection algorithm which is hybrid and multi-scale. First, it relies on a connected component-based approach: After the segmentation of the image, a classification step using a new wavelet descriptor spots the letters. A new graph modeling and its traversal procedure allow to form candidate text areas. Second, a texture-based approach discards the false positives. Finally, the detected text areas are precisely cut out and a new binarization step is introduced. The main advantage of our method is that few assumptions are put forward. Thus, “challenging texts” like multi-sized, multi-colored, multi- oriented or curved text can be localized. The efficiency of TextCatcher has been validated on three different datasets: Two come from the ICDAR competition, and the third one contains photographs we have taken with various daily life texts. We present both qualitative and quantitative results.