Text quality estimation in video

Text quality can significantly affect the results of text detection and recognition in digital video. In this paper we address the problem of estimating text quality. The quality of text that appears in video is often much lower than that in document images, and can be degraded by factors such as low resolution, background variation, uneven lighting, motion of the text and camera, and in the case of scene text, projection from 3D. Features based on text resolution, background noise, contrast, illumination and texture are selected to describe the text quality, normalized and fed into a trained RBF network to estimate the text quality. The performance using different training schemes are compared.