Robust Binarization for Video Text Recognition

Z. Saïdane, C. Garcia, Orange Labs, 2007

We present an automatic binarization method for color text areas in images or videos, which is robust to complex background, low resolution or video coding artefacts. Based on a specific architecture of convolutional neural networks, the proposed system automatically learns how to perform binarization, from a training set of synthesized text images and their corresponding desired binary images, without making any assumptions or using tunable parameters. The proposed method is compared to state-of-the-art binarization techniques, with respect to Gaussian noise and contrast variations, demonstrating the robustness and the efficiency of our method. Text recognition experiments on a database of images extracted from video frames and web pages, with two classical OCRs applied on the obtained binary images show a strong enhancement of the recognition rate by more than 40%.