Illumination Invariant Text Recognition System Based On Contrast Limit Adaptive Histogram Equalization in Videos/Images

The textual information present in the image/video plays a major role in understanding scene/video. Tn this paper, we propose a system for detecting the text in videos possessing varying illumination based on Contrast Limit Adaptive Histogram Equalization(CLAFIE). Certain heuristic rules are applied to the preprocessed video frame to detect the text clusters. Further, the newly designed geometrical rules and morphological operations are employed OH the obtained text detection results for text localization. Once the text gets localized, the text lines are segmented followed by character segmentation. The segmented characters are then recognized using OCR and then synthesized as voice output message which is audible so that one can hear and understand the text content present in videos. The experimental results obtained on publicly available standard datasets, TRECVID video dataset and our own video dataset illustrate that the proposed method can detect, localize and recognize the texts of various sizes, fonts and colors.