An overview is presented of algorithms and techniques for document image analysis with an emphasis on those for grnphics recognition and interpretation. The techniques are derived from the fields of image processing. pattern recognition, and machine vision. The objective in document image analysis is to recognize page contents including layout, text, and figures. Although optical character reco...

Document Image Analysis recognizes text and graphics in documents acquired as images. An approach without Optical Character Recognition (OCR) for degraded document image analysis has been adopted in this paper. The technique involves document imaging methods such as Image Fusing and Speeded Up Robust Features (SURF) Detection to identify and extract the degraded regions from a set of document i...

Document images produced by scanner or digital camera, usually suffer from geometric and photometric distortions. Both of them deteriorate the performance of OCR systems. In this paper, we present a novel method to compensate for undesirable geometric distortions aiming to improve OCR results. Our methodology is based on finding text lines by dynamic local connectivity map and then applying a l...

Documents Image Binarization is performed in the preprocessing stage for document analysis and it aims to segment the foreground text from the document background. A fast and accurate document image binarization technique is important for the ensuing document image processing tasks such as optical character recognition (OCR). Though document image binarization has been studied for many years, t...

A document image analysis toolbox, including a collection of data structures and algorithms to suppbrt a variety of applications, is described in this paper. An experimental environment is built to allow developers to develop, test and optimize their algorithms and systems. Appropriate and quantitative performance metrics for each kind of information a document analysis technique infers have be...