University of Houston

Digital videos have been extensively used for educational purposes and distance learning. Tablet PC based lecture videos have been commonly used at UH for many years. To enhance the user experience and improve usability of classroom lecture videos, we designed an indexed, captioned and searchable (ICS) video player. Video indexing allows users to easily navigate the video to find what they are looking for. Users could also search for a topic inside video lectures.

Searching inside of a lecture is useful especially for long videos; instead of losing an hour watching the entire video, it will allow us to find the relevant scenes instantly. This future requires extracting the text from video screenshots by using Optical Character Recognition (OCR). Since ICS video frames include complex images, graphs, and shapes in different colors with non-uniform backgrounds, our text detection requires a more specialized approach than is provided by off-the-shelf OCR engines, which are designed primarily for recognizing text within scanned documents in black and white format.

In this thesis, we describe how we used and increased the detection of these OCR engines for ICS video player. We surveyed the current OCR engines for ICS video frames and realized that accuracy of recognition could/should be increased by preprocessing the images. By using some image processing techniques such as resizing, segmentation, smoothing, inversion on images , we increased the accuracy rate of search in ICS video player.