Software System for Vocal Rendering of Printed Documents

The objective of this paper is to present a software system architecture developed to render the printed documents in a vocal form. On the other hand, in the paper are described the software solutions that exist as software components and are necessary for documents processing as well as for multimedia device controlling used by the system. The usefulness of this system is for people with visual disabilities that can access the contents of documents without that they be printed in Braille system or to exist in an audio form.accessibility, TWAIN, OCR, TTS, SAPI

A novel approach for skew estimation of document images in OCR system

Optical character recognition (OCR) is an area which has always received special attention. OCR systems are typically built on the strategy of divide and conquer, rather than recognizing documents at one go. They utilize several stages during the course of recognition. There have been many stages in a typical OCR system, preprocessing stage in considered to be indispensable. An input image or information need to be normalized and converted into format acceptable by OCR system. OCR systems typically assume that documents were printed with a single direction of the text and that the acquisition process did not introduce a relevant skew. Practically this assumption is not very strong and printed document could be skewed at some angle with horizontal axis. In this paper, we have proposed a new technique for skew estimation of image document. In the proposed scheme, multiscale properties of an image are utilized together with principal component analysis to estimate the orientation of principal axis of clustered data

Automated framework for robust content-based verification of print-scan degraded text documents

Fraudulent documents frequently cause severe financial damages and impose security breaches to civil and government organizations. The rapid advances in technology and the widespread availability of personal computers has not reduced the use of printed documents. While digital documents can be verified by many robust and secure methods such as digital signatures and digital watermarks, verification of printed documents still relies on manual inspection of embedded physical security mechanisms.The objective of this thesis is to propose an efficient automated framework for robust content-based verification of printed documents. The principal issue is to achieve robustness with respect to the degradations and increased levels of noise that occur from multiple cycles of printing and scanning. It is shown that classic OCR systems fail under such conditions, moreover OCR systems typically rely heavily on the use of high level linguistic structures to improve recognition rates. However inferring knowledge about the contents of the document image from a-priori statistics is contrary to the nature of document verification. Instead a system is proposed that utilizes specific knowledge of the document to perform highly accurate content verification based on a Print-Scan degradation model and character shape recognition. Such specific knowledge of the document is a reasonable choice for the verification domain since the document contents are already known in order to verify them.The system analyses digital multi font PDF documents to generate a descriptive summary of the document, referred to as \Document Description Map" (DDM). The DDM is later used for verifying the content of printed and scanned copies of the original documents. The system utilizes 2-D Discrete Cosine Transform based features and an adaptive hierarchical classifier trained with synthetic data generated by a Print-Scan degradation model. The system is tested with varying degrees of Print-Scan Channel corruption on a variety of documents with corruption produced by repetitive printing and scanning of the test documents. Results show the approach achieves excellent accuracy and robustness despite the high level of noise

An efficient scheme for tilt correction in Arabic OCR system

Preprocessing stage is required in almost every image processing application ranging from biometric analysis to document image analysis. An input image or information need to be normalized and converted into format acceptable by OCR (optical character recognition) system. OCR systems typically assume that documents were printed with a single direction of the text and that the acquisition process did not introduce a relevant skew. Practically this assumption is not very strong and printed documents could be skewed at some angle with horizontal axis. In this paper, we have proposed skew estimation of document images for Arabic fonts. It is based upon the specific feature of Arabic script. In our proposed scheme, we scan for the occurrence of letter 'alif' and estimate the tilt based upon its slope. Extensive experimentation was performed and scheme was found to be very effective

Examining and improving the effectiveness of relevance feedback for retrieval of scanned text documents

Important legacy paper documents are digitized and collected in online accessible archives. This enables the preservation, sharing, and significantly the searching of these documents. The text contents of these document images can be transcribed automatically using OCR systems and then stored in an information retrieval system. However, OCR systems make errors in character recognition which have previously been shown to impact on document retrieval behaviour. In particular relevance feedback query-expansion methods, which are often effective for improving electronic text retrieval, are observed to be less reliable for retrieval of scanned document images. Our experimental examination of the effects of character recognition errors on an ad hoc OCR retrieval task demonstrates that, while baseline information retrieval can remain relatively unaffected by transcription errors, relevance feedback via query expansion becomes highly unstable. This paper examines the reason for this behaviour, and introduces novel modifications to standard relevance feedback methods. These methods are shown experimentally to improve the effectiveness of relevance feedback for errorful OCR transcriptions. The new methods combine similar recognised character strings based on term collection frequency and a string edit-distance measure. The techniques are domain independent and make no use of external resources such as dictionaries or training data

Use of colour for hand-filled form analysis and recognition

Colour information in form analysis is currently under utilised. As technology has advanced and computing costs have reduced, the processing of forms in colour has now become practicable. This paper describes a novel colour-based approach to the extraction of filled data from colour form images. Images are first quantised to reduce the colour complexity and data is extracted by examining the colour characteristics of the images. The improved performance of the proposed method has been verified by comparing the processing time, recognition rate, extraction precision and recall rate to that of an equivalent black and white system