The extraction of text in an image is a classical problem in the computer vision. Extraction involves detection, localization, tracking, extraction, enhancement and recognition of the text from the given image. However variation of text due to difference in size, style, orientation, alignment, low image contrast and complex background make the problem of automatic text extraction extremely chal...

With the rapid growth of camera-based mobile devices, applications that answer questions such as, “What does this sign say?" are becoming increasingly popular. This is related to the problem of optical character recognition (OCR) where the task is to recognize text occurring in images. The OCR problem has a long history in the computer vision community. However, the success of OCR systems is la...

In this article, we describe the FINT system, which stands for Find Images aNd Text. This system is built within the VindIT project, that focuses on handling large amounts of multi-media data. The current approach concentrates on searching in a combination of textual and visual data. The system described here is an iterative system that computes distances between the images. From each image (an...

Text localization is the first step when automatically reading text in images. Since existing methods often fail when applied to unconstrained images, in this paper we propose a more robust approach exploiting different kind of information. In particular, we first extract textural features, a combination of a stroke filter with a super-pixel segmentation, and then search for connected component...

This paper introduces a method for the automatic discrimination of digital images based on their semantic content. The proposed system allows to detect if a digital image contains or not a text. This is realized by a multi-steps procedure based on low-level features set properly derived. Our experiments show that the proposed algorithm is competitive in efficiency with classical techniques, and...

Recently, there is an increasing need to share medical images for research purpose. In order to respect and preserve patient privacy, most of the medical images are de-identified with protected health information (PHI) before research sharing. Since manual de-identification is time-consuming and tedious, so an automatic de-identification system is necessary and helpful for the doctors to remove...