The amount of digital video data is increasing over the world. It highlights the need for efficient algorithms that can index, retrieve and browse this data by content. This can be achieved by identifying semantic description captured automatically from video structure. Among these descriptions, text within video is considered as rich features that enable a good way for video indexing and brows...

With the dramatic increase in multimedia data, escalating trend of internet, and amplifying use of image/video capturing devices; content based indexing and text extraction is gaining more and more importance in research community. In the last decade, many techniques for text extraction are reported in the literature. Methodologies of text extraction from images/videos is generally comprises of...

We propose a camera-based assistive text reading framework to help blind persons read text labels and product packaging from hand-held objects in their daily lives. We first propose an efficient and effective motion based method to define a region of interest (ROI) in the video by asking the user to shake the object. To automatically localize the text regions from the object ROI, we propose a n...

Automatically localizing a position based on few natural language instructions is essential for future robots to communicate and collaborate with humans. To approach this goal, we focus text-to-point-cloud cross-modal localization problem. Given textual query, it aims identify the described location from city-scale point clouds. The task involves two challenges. 1) In clouds, similar ambient in...

Video text information plays an important role in semantic-based video analysis, indexing and retrieval. Video texts are closely related to the content of a video. Usually, the fundamental steps of text-based video analysis, browsing and retrieval consist of video text detection, localization, tracking, segmentation and recognition. Video sequences are commonly stored in compressed formats wher...

Text localization from scene images is a challenging task that finds application in many areas. In this work, we propose a novel hybrid text localization approach that exploits Multi-resolution Maximally Stable Extremal Regions to discard false-positive detections from the text confidence maps generated by a Fast Feature Pyramid based sliding window classifier. The use of a multi-scale approach...

Comic books constitute an important cultural heritage asset in many countries. Digitization combined with subsequent document understanding enable direct content-based search as opposed to metadata only search (e.g. album title or author name). Few studies have been done in this direction. In this work we detail a novel approach for the automatic text localization in scanned comics book pages, ...

BACKGROUND Figures are ubiquitous in biomedical full-text articles, and they represent important biomedical knowledge. However, the sheer volume of biomedical publications has made it necessary to develop computational approaches for accessing figures. Therefore, we are developing the Biomedical Figure Search engine (http://figuresearch.askHERMES.org) to allow bioscientists to access figures ef...

Based on feature matching of car tail text information, a novel approach for vehicle identification is proposed in this paper. Our method creatively implements Scale-Invariant Feature Transform (SIFT) to extract distinctive invariant features from car tail text sub-images, which is a new application of SIFT. In our approach, firstly, the coordinate and the content of a vehicle’s license plate a...