Recognition of characters in document images using morphological operation

In this paper, we deal with the problem of document image rectification from image captured by digital cameras. The improvement on the resolution of digital camera sensors has brought more and more applications for non-contact text capture. It is widely used as a form of data entry from some sort of original paper data source, documents, sales receipts or any number of printed records. It is crucial to the computerization of printed texts so that they can be electronically searched, stored more compactly, displayed on-line, and used in machine processes such as machine translation, text-to-speech and text mining. Unfortunately, perspective distortion in the resulting image makes it hard to properly identify the contents of the captured text using traditional optical character recognition (OCR) systems. In this work we propose a new technique; it is a system that provides a full alphanumeric recognition of printed or handwritten characters at electronic speed by simply scanning the form. Optical character recognition, usually abbreviated as OCR is the mechanical or electronic conversion of scanned images of handwritten, typewritten or printed text into machine-encoded text. OCR software detects and extracts each character in the text of a scanned image, and using the ASCII code set, which is the American Standard Code for Information Interchange, converts it into a computer recognizable character. Once each character has been converted, the whole document is saved as an editable text document with a highest accuracy rate of 99.5 per cent, although it is not always this accurate. The basic idea of Optical Character Recognition (OCR) is to classify optical patterns (often contained in a digital image) corresponding to alphanumeric or other characters

Adaptive Methods for Robust Document Image Understanding

A vast amount of digital document material is continuously being produced as part of major digitization efforts around the world. In this context, generic and efficient automatic solutions for document image understanding represent a stringent necessity. We propose a generic framework for document image understanding systems, usable for practically any document types available in digital form. Following the introduced workflow, we shift our attention to each of the following processing stages in turn: quality assurance, image enhancement, color reduction and binarization, skew and orientation detection, page segmentation and logical layout analysis. We review the state of the art in each area, identify current defficiencies, point out promising directions and give specific guidelines for future investigation. We address some of the identified issues by means of novel algorithmic solutions putting special focus on generality, computational efficiency and the exploitation of all available sources of information. More specifically, we introduce the following original methods: a fully automatic detection of color reference targets in digitized material, accurate foreground extraction from color historical documents, font enhancement for hot metal typesetted prints, a theoretically optimal solution for the document binarization problem from both computational complexity- and threshold selection point of view, a layout-independent skew and orientation detection, a robust and versatile page segmentation method, a semi-automatic front page detection algorithm and a complete framework for article segmentation in periodical publications. The proposed methods are experimentally evaluated on large datasets consisting of real-life heterogeneous document scans. The obtained results show that a document understanding system combining these modules is able to robustly process a wide variety of documents with good overall accuracy

The rectification and recognition of document images with perspective and geometric distortions

Ph.DDOCTOR OF PHILOSOPH

Extraction of Text from Images and Videos

Ph.DDOCTOR OF PHILOSOPH