Text Extraction plays a major role in finding vital and valuable information. Text extraction involves detection, localization, tracking, binarization, extraction, enhancement and recognition of the text from the given image. These text characters are difficult to be detected and recognized due to their deviation of size, font, style, orientation, alignment, contrast, complex colored, textured ...

[PDF] [Full Text] [Abstract] , October 27, 2009; 106 (43): 18362-18366. PNAS Shawn A. Steckenfinger and Asif A. Ghazanfar Monkey visual behavior falls into the uncanny valley [PDF] [Full Text] [Abstract] , October 13, 2010; 30 (41): 13609-13623. J. Neurosci. and Jon Driver Toemme Noesselt, Sascha Tyll, Carsten Nicolas Boehler, Eike Budinger, Hans-Jochen Heinze Visual Detection Sensitivity S...

Document Image Binarization converts a gray-scale document image into binary document image .It is usually performed in the pre-processing stage of document image analysis and it aims to segment the foreground text from the document background. Segmentation of foreground text from the document background is a difficult task in the case of degraded document images. In this paper we propose a sim...

In this paper, we propose a refined scene text detector with a novel Feature Enhancement Network (FEN) for Region Proposal and Text Detection Refinement. Retrospectively, both region proposal with only 3 × 3 sliding-window feature and text detection refinement with single scale high level feature are insufficient, especially for smaller scene text. Therefore, we design a new FEN network with ta...

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...

Text detection is fundamental to video information retrieval and indexing. Existing methods cannot handle well those texts with different contrast or embedded in a complex background. To handle these difficulties, this paper proposes an efficient text detection approach, which is based on invariant features, such as edge strength, edge density, and horizontal distribution. First, it applies edg...

This chapter deals with digital restoration, preservation, and data base storage of historical manuscripts images. It focuses on restoration techniques and binarization methods combined with image processing applied on document images for text background enhancement and discrimination. Sequential image processing procedures are applied for image refinement and enhancement on quality class categ...

The aim of image Augmentation is to improve the interpretability or perception of information in images for human viewers, or to provide `better' input for other automated image processing techniques. The aim is to improve the visual appearance of an image, or to provide a “better transform representation for future automated image processing”. This paper is about a review of histogram techniqu...

We present a multiresolutional algorithm that segments a compound document and uses the results of the segmentation for document enhancement in copier applications. The document is initially segmented into halftone and non-halftone areas. Based on this segmentation the location of the edges due to text, graphics, and images (and not due to halftone dots) are detected on halftone as well as on n...