Text localization in images using reverse thresholds algorithm

High color similarity between text pixels and background pixels is the major problem that causes failure during text localization. In this paper, a novel algorithm, Reverse Thresholds (RT) algorithm is proposed to localize text from the images with various text-background color similarities. First, a rough calculation is proposed to determine the similarity index for every text region. Then, by applying reverse operation, the best thresholds for each text region are calculated by its similarity index. To remove other uncertainties, self-generated images with the same text features but different similarity index are used as experiment dataset. Experiment result shows that RT algorithm has higher localizing strength which is able to localize text in a wider range of similarity index

Snoopertrack: Text Detection And Tracking For Outdoor Videos

In this work we introduced SnooperTrack, an algorithm for the automatic detection and tracking of text objects - such as store names, traffic signs, license plates, and advertisements - in videos of outdoor scenes. The purpose is to improve the performances of text detection process in still images by taking advantage of the temporal coherence in videos. We first propose an efficient tracking algorithm using particle filtering framework with original region descriptors. The second contribution is our strategy to merge tracked regions and new detections. We also propose an improved version of our previously published text detection algorithm in still images. Tests indicate that SnooperTrack is fast, robust, enable false positive suppression, and achieved great performances in complex videos of outdoor scenes. © 2011 IEEE.505508IEEE,IEEE Signal Processing SocietyJung, K., Kim, K.I., Jain, A.K., Text information extraction in images and video: A survey (2004) Pattern Recognition, 37 (5), pp. 977-997. , DOI 10.1016/j.patcog.2003.10.012, PII S0031320303004175Lucas, S.M., ICDAR 2005 Text locating competition results (2005) ICDAR, pp. 80-84Epshtein, B., Ofek, E., Wexler, Y., Detecting text in natural scenes with stroke width transform (2010) IEEE CVPR, pp. 886-893Pan, Y.F., Liu, C.L., Hou, X., Fast scene text localization by learning-based filtering and verification (2010) IEEE ICIPDinh, T.N., Park, J., Lee, G., Text localization using image cues and text line information (2010) IEEE ICIPMinetto, R., Thome, N., Cord, M., Fabrizio, J., Marcotegui, B., Snoopertext: A multiresolution system for text detection in complex visual scenes (2010) IEEE ICIP, pp. 1-4Gllavata, J., Ewerth, R., Freisleben, B., Tracking text in mpeg videos ACM International Conference on Multimedia, 2004, pp. 240-243Qian, X., Liu, G., Wang, H., Su, R., Text detection, localization, and tracking in compressed video (2007) Elsevier - Image Communication, 22, pp. 752-768Na, Y., Wen, D., An effective video text tracking algorithm based on sift feature and geometric constraint Advances in Multimedia Information Processing, 2010, pp. 392-403Dalal, N., Triggs, B., Histograms of oriented gradients for human detection (2005) IEEE CVPR, pp. 886-893Chen, X., Yuille, A.L., Detecting and reading text in natural scenes (2004) IEEE CVPR, pp. 366-373Isard, M., Blake, A., Condensation - Conditional density propagation for visual tracking (1998) International Journal of Computer Vision, 29, pp. 5-28Thome, N., Mérad, D., Miguet, S., Learning articulated appearance models for tracking humans: A spectral graph matching approach (2008) Signal Processing: Image Communication, 23, pp. 769-787Okuma, K., Taleghani, A., De Freitas, N., Little, J.J., Lowe, D.G., A boosted particle filter: Multitarget detection and tracking (2004) ECCV, pp. 28-3