Machine Learning in OCR Technology: Performance Analysis of Different OCR Methods for Slide-to-Text Conversion in Lecture Videos

Abstract

A significant percentage of a lecture video's content shown is text. Video text can therefore be a crucial source for automated video indexing. Researchers have recognised printed and handwritten text extracted from pictures using a variety of machine learning techniques and tools before digitising it. A machine learning technology called optical character recognition (OCR) enables us to recognise and retrieve text information from documents, converting it into searchable and editable data. This study primarily focuses on text extraction from lecture slides using Google Cloud Vision (GCV), Tesseract, Abbyy Finereader, and Transym OCR and compares the results to develop a lecture video indexing scheme for the non-linear steering in lecture videos to watch only the interesting points of topics. We have taken a total of 438 key-frames in 10 categories from seven different lecture videos that range in length. First, binary and greyscale versions of the input colour images are created. Before using the OCR APIs, the frames are additionally preprocessed to improve the image quality. The recognition accuracy demonstrated that the GCV OCR performs effectively, saving computing time by collecting image text with the highest accuracy of other tools, 96.7 percent.