Deep learning

Pattern recognition (psychology)

Meaning (existential)

Text detection

Multimedia

Psychotherapist

Image (mathematics)

Geodesy

Variety (cybernetics)

Machine learning

Benchmark (surveying)

Artificial intelligence

Psychology

Geography

Information retrieval

Font

Computer science

Convolutional neural network

Journal of Computing in Higher Education

Automated text detection and analysis holds incredible potential for research in higher education. It is challenging because higher education institutes produce an enormous amount and variety of texts, letters, articles, books, reports etc. Futuristic E-learning based education replaces the difficulty of understanding the semantic meaning of the learning content from videos which is most prominent source used by the leaners to acquire knowledge. Therefore, Content Based Video retrieval has become the challenging research area under pattern recognition and computer vision in higher education through Massive Open Online Courses (MOOCs). Text plays a dynamic role in understanding the true meaning of behavior of the video. Hence, it is challenging to detect and identify the text in video due to variable complex background, low contrast, blur, poor illumination, font size, font-style, occlusions. The traditional approach of end-to-end convolution neural network (CNN) performs satisfactory in detecting video text. However, it is also important to deal with the video size, therefore, we have adopted Map Reduce technique to store the video content and utilize it efficiently by parallel computing. Followed by this, we employed novel approach to clean up the video frames to feed to neural network model based on region proposal network (RPN) with CNN by finding appropriate anchor ratios to extract the text candidates. Finally, we train our model with extracted frames to predict for the test videos. The proposed method is evaluated on ICDAR Video text benchmark datasets and few publicly available test datasets to achieve high recall.

Automated text detection from big data scene videos in higher education: a practical approach for MOOCs case study