[Lrde Annonce] New journal publication: TextCatcher: a method to detect curved and challenging text in natural scenes (IJDAR)

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.