Text extraction from videos using a hybrid approach

Abstract

With rapid intensification of existing multimedia documents and mounting demand for information indexing and retrieval, much endeavor has been done on extracting the text from images and videos. Text extraction in video documents, as a momentous research division of content-based information retrieval and indexing, continues to be a topic of much interest to researchers. Text extracting is demanding owing to a range of setbacks like complex background, varying font size, different style, lower resolution and blurring, position, viewing angle and so on. In this paper we propose a hybrid method where the two most well-liked text extraction methods explicitly region based method and connected component (CC) based method comes together. The former method is used to obtain the text prevailing confidence where as the latter is used for text extraction and grouping. The video splitting and key frame detection is followed by the preprocessing to designate the text region indicator. The extracted features are scrutinized using artificial neural network as the classifier and lastly grouped into words/lines based on the bounding box distance. We evaluated the performance of the proposed approach on various videos and obtained considerable results when weighed against the existing methods.