Similar articles

Abstract: We present a robust connected-component (CC) based method for automatic detection and segmentation of text in real-scene images. This technique can be applied in robot vision, sign recognition, meeting processing and video indexing. First, a Non-Linear Niblack method (NLNiblack) is proposed to decompose the image into candidate CCs. Then, all these CCs are fed into a cascade of classifiers trained by adaboost algorithm. Each classifier in the cascade responds to one feature of the CC. Proposed here are 12 novel features which are insensitive to noise, scale, text orientation and text language. The classifier cascade allows non-text CCs of the image to be rapidly discarded while more computation is spent on promising text-like CCs. The CCs passing through the cascade are considered as text components and are used to form the segmentation result. A prototype system was built, with experimental results proving the effectiveness and efficiency of the proposed method.

[1] Chen, B.T., Bae, Y., Kim, T.Y., 1999. Automatic Text Extraction in Digital Videos Using FFT and Neural Network. Proceedings of the IEEE International Fuzzy Systems Conference. Seoul, Korea.

[2] Clark, P., Mirmehdi, M., 2000. Finding Text Regions Using Localised Measures. Proceedings of the 11th British Machine Vision Conference, p.675-684.

[3] Doermann, D., Liang, J., Li, H., 2003. Progress in Camera-based Document Image Analysis. Proceedings of the 7th International Conference on Document Analysis and Recognition. Edinburgh, Scotland, 1:606-616.

[4] Ferreira, S., Thillou, C., Gosselin, B., 2003. From Picture to Speech: An Innovative OCR Application for Embedded Environment. ProRISC 2003. Veldhoven, Netherland.

[5] Gao, J., Yang, J., 2001. An Adaptive Algorithm for Text Detection from Natural Scenes. CVPR 2001, p.84-89.

[6] Haritaoglu, I., 2001. Scene Text Extraction and Translation for Handheld Devices. CVPR 2001, p.408-413.

[7] Hasan, Y.M.Y., Karam, L.J., 2000. Morphological text extraction from images. IEEE Transactions on Image Processing, 9(11):1978-1983.

[8] Jie, S., Rehg, J.M., Bobick, A., 2004. Automatic Cascade Training with Perturbation Bias. CVPR 2004, 2:276-283.

[9] Li, H., Doermann, D., Kia, O., 2000. Automatic text detection and tracking in digital video. IEEE Transactions on Image Processing, 9(1):147-156.

[10] Shin, C.S., Kim, K.I., Park, M.H., Kim, H.J., 2000. Support Vector Machine-based Text Detection in Digital Video. Proceedings of the IEEE Signal Processing Society Workshop on Neural Networks for Signal Processing X, 2:634-641.

[11] Viola, P., Jones, M., 2001. Robust Real-time Face Detection. ICCV01, 2:747.

[12] Wang, K., Kangas, J., 2003. Character location in scene images from digital camera. Pattern Recognition, 36(10):2287-2299.

[13] Winger, L., Robinson, J.A., Jernigan, M.E., 2000. Low-complexity character extraction in low-contrast scene images. International Journal of Pattern Recognition and Artificial Intelligence, 14(2):113-135.

[14] Zhong, Y., Zhang, H.J., Jain, A.K., 2000. Automatical caption localization in compressed video. IEEE Transactions on PAMI, 22(4):385-392.