In the past few years, text in natural scene images has gained potential to be a key feature for content based retrieval. They can be extracted and used in search engines, providing relevant information about the images. Robust and efficient techniques from the document analysis and the vision community were borrowed to solve the challenge of digitizing text in such images in the wild. In this ...

State-of-the-art blind image deconvolution approaches have difficulties when dealing with text images, since they rely on natural image statistics which do not respect the special properties of text images. On the other hand, previous document image restoring systems and the recently proposed black-andwhite document image deblurring method [1] are limited, and cannot handle large motion blurs a...

An Android mobile visual text translator is proposed and developed. The translator is able to extract text from images and perform in situ translation. Our app can automatically recognize texts in simple plain images such as book, sign, and map and overlays the translated text on top of the original one, while preserving information such as location, color, font size, etc. This is often needed ...

Extracting text character from natural scene images is a challenging problem due to differences in text style, font, size, orientation, alignment and complex background. The text data present in images and video contain certain useful information for content-based information indexing and retrieval, sign translation and intelligent driving assistance. In scene text extraction, adjacent characte...

Two studies examined user engagement in two types of multimedia training systems -a more passive medium, videotape, and a less passive medium, interactive software. Each study compared engagement in three formats: the Text format contained text and still images, the Audio format contained audio and still images, and the Video format contained audio and video images. In both studies, engagement ...

In this paper we introduce a new technique for presenting textual information within images and for enabling users to interact with these texts. Our method relies on shading images using text-based dither matrices; users extract text by effectively enlarging the dither matrices to the point where text becomes legible. Transitions between matrix sizes are carried out step by step and can be impl...

This paper presents a methodology for document processing, by separating text paragraphs from images. The methodology is based on the recognition of text characters and words for the efficient separation text paragraphs from images by keeping their relationships for a possible reconstruction of the original page. The text separation and extraction is based on a hierarchical framing process. The...

Because of the size of the World Wide Web and its inherent lack of structure, finding what one is looking for can be a challenge. In fact, some of the most highly visited Web sites are search engines. However, while Web pages typically contain both text and images, most currently available search engines only index text. This paper describes WebSeer, a system for locating images on the Web. Web...

This paper presents the results of a new approach for binarization of text images. The new technique uses the fuzzy C-means clustering algorithm to simulate the clustering performed by the human visual system. The clustering process was applied to eleven text images. All of them have black text but the background change in color. As a mean of comparison, binarization using the image histogram w...

This paper presents a data model for images immersed in the world wide web and that derive their meaning from visual similarity, from the connection with the text of the pages that contains them, and from the link structure of the web. I will model images on the web as a graph whose nodes are either text documents or images, and whose edges are links, labeled with measures of relevance of one d...