In biomedical publications, figures and images concisely summarize a paper's experimental findings and results. Recent studies have therefore explored the use of images to assist in information retrieval (IR) in biomedicine, mostly based on mining the image caption content. We extend this approach by mining the image text, which refers to the text inside biomedical figures and images. In this w...

A document conveys information using multiple modalities, including text, layout/style and images. For example, journal articles usually have figures to illustrate experimental results, and the title in a journal article usually has a different font size than the body text. Indexing and retrieval using only text is the traditional way of IR (Information Retrieval). With the development of the I...

In this paper, compression scheme is presented for Indian Language handwritten text document images. Document image compression is an active area of research. Current OCR technology is not effective for handling the handwritten text images. The proposed compression scheme deals with the handwritten gray level document in Devnagri script. The method is based on the separation of foreground and b...

Text data present in images contain useful information for automatic explanation, indexing, and structuring of images. Extraction of this information involves detection, localization, tracking, extraction, enhancement, and recognition of the text from a given image. However variations of text due to differences in size, style, orientation, and alignment, as well as low image contrast and comple...

Using both text and image content features, a hybrid image retrieval system for Word Wide Web is developed in this paper. We first use a text-based image metasearch engine to retrieve images from the World Wide Web based on the text information on the image host pages to provide an initial image set. Because of the high-speed and low cost nature of the text-based approach, we can easily retriev...

This paper presents a one pass block classification algorithm for efficient coding of compound images which consists of multimedia elements like text, graphics and natural images. The objective is to minimize the loss of visual quality of text during compression by separating text information which needs high special resolution than the pictures and background. It segments computer screen image...

We address the problem of detecting and recognizing the text embedded in online images that are circulated over the Web. Our idea is to leverage context information for both text detection and recognition. For detection, we use local image context around the text region, based on that the text often sequentially appear in online images. For recognition, we exploit the metadata associated with t...

A deep convolutional neural network model is presented here which uses learning features for text and non-text region segmentation from document images. The key objective to extract regions the complex layout images without any prior knowledge of segmentation. In a real-world scenario, or magazine contain various information along with such as symbols, logos, pictures, graphics. Extraction chal...

Modern search engines rely solely on text to analyze the content of Web documents. However, it is well known that humans often incorporate ”mental visualization” in the form of mental images in order to interpret text. Psychological studies have demonstrated that humans are able to create these visual perceptions even in absence of external visual stimuli. Such a physiological behavior in the h...

We describe a segmentation method and associated file format for storing images of color documents. We separate each page of the document into three layers, containing the background (usually one or more photographic images), the text, and the color of the text. Each of these layers has different properties, making it desirable to use different compression methods to represent the three layers....