Recognition of Video Text through Temporal Integration

This paper presents a method for temporal integration, which can be used to improve the recognition accuracy of video texts. Given a word detected in a video frame, we use a combination of Stroke Width Transform and SIFT (Scale Invariant Feature Transform) to track it both backward and forward in time. The text instances within the word's frame span are then extracted and aligned at pixel level. In the second step, we integrate these instances into a text probability map. By thresholding this map, we obtain an initial binarization of the word. In the final step, the shapes of the characters are refined using the intensity values. This helps to preserve the distinctive character features (e.g., sharp edges and holes), which are useful for OCR engines to distinguish between the different character classes. Experiments on English and German videos show that the proposed method outperforms existing ones in terms of recognition accuracy.