Optimized searching of video based on speech and video text content

Digitalized video has become an established storage and exchange medium due to the fast development in recording technology. E-lecturing has become added professional popular. The number of lecture video data on the web is growing quickly. Therefore, an additional skillful method for video retrieval within huge lecture video archives is immediately desired. This paper comes close to regular video indexing and video stalk in large lecture videos collection. We apply Automatic video segmentation and key-frame detection to present an image instruction for the video content navigation. In this process, video will be retrieved based on content based video search for the input of speech and video text content. Histogram of gradient and Support Vector Machine is used for the feature extraction from the video and to get the accurate result respectively. This will collect required features from lecture video database as labeled with speech recognition and also retrieve lecture video from database as better than existing algorithm. Hence the proposed system will be more efficient for retrieving the videos and also improves the recognition rate.