The Experts below are selected from a list of 32739 Experts worldwide ranked by ideXlab platform

Neil W. Bergmann - One of the best experts on this subject based on the ideXlab platform.

  • Arabic optical character recognition system using recognition-Based Segmentation
    Pattern Recognition, 2001
    Co-Authors: Anthony Cheung, Mohammed Bennamoun, Neil W. Bergmann
    Abstract:

    Optical character recognition (OC) systems improve human-machine interaction and are widely used in many areas. The recognition of cursive scripts is a difficult task as their Segmentation suffers from serious problems. This paper proposes an Arabic OCR system, which uses a recognition-Based Segmentation Technique to overcome the classical Segmentation problems. A newly developed Arabic word Segmentation algorithm is also introduced to separate horizontally overlapping Arabic words/subwords. There is also a feedback loop to control the combination of character fragments for recognition. The system was implemented and the results show a 90% recognition accuracy with a 20 chars/s recognition rate.

Raed Abu Zitar - One of the best experts on this subject based on the ideXlab platform.

  • development of an efficient neural Based Segmentation Technique for arabic handwriting recognition
    Pattern Recognition, 2010
    Co-Authors: Husam Al Hamad, Raed Abu Zitar
    Abstract:

    Off-line Arabic handwriting recognition and Segmentation has been a popular field of research for many years. It still remains an open problem. The challenging nature of handwriting recognition and Segmentation has attracted the attention of researchers from industry and academic circles. Recognition and Segmentation of Arabic handwritten script is a difficult task because the Arabic handwritten characters are naturally both cursive and unconstrained. The analysis of Arabic script is more complicated in comparison with English script. It is believed, good Segmentation is one reason for high accuracy character recognition. This paper proposes and investigates four main Segmentation Techniques. First, a new feature-Based Arabic heuristic Segmentation AHS Technique is proposed for the purpose of partitioning Arabic handwritten words into primitives (over-Segmentations) that may then be processed further to provide the best Segmentation. Second, a new feature extraction Technique (modified direction features-MDF) with modifications in accordant with the characteristics of Arabic scripts is also investigated for the purpose of segmented character classification. Third, a novel neural-Based Technique for validating prospective Segmentation points of Arabic handwriting is proposed and investigated Based on direction features. In particular, the vital process of handwriting Segmentation is examined in great detail. The classifier chosen for Segmentation point validation is a feed-forward neural network trained with the back-propagation algorithm. Many experiments were performed, and their elapsed CPU times and accuracies were reported. Fourth, new fusion equations are proposed and investigation to examine and evaluate a prospective Segmentation points by obtaining a fused value from three neural confidence values obtained from right and center character recognition outputs in addition to the Segmentation point validation (SPV) output. Confidence values are assigned to each Segmentation point located through feature detection. All Techniques components are tested on a local benchmark database. High Segmentation accuracy is reported in this research along with comparable results for character recognition and Segmentation.

Waleed Alnuaimy - One of the best experts on this subject based on the ideXlab platform.

  • detection of exudates from digital fundus images using a region Based Segmentation Technique
    European Signal Processing Conference, 2011
    Co-Authors: Hussain F Jaafar, Asoke K Nandi, Waleed Alnuaimy
    Abstract:

    The detection and quantification of exudates can contribute to the mass screening of the diabetic retinopathy, the major cause of blindness. In this work, we outline detection Techniques of the main retinal structures, namely the blood vessels, optic disk and fovea. A new method for the detection of exudates using adaptive thresholding and classification is proposed in which the retinal structures are used to remove artefacts from exudate detection results. The proposed adaptive thresholding proceeds through two stages: (1) image decomposition into a number of homogeneous sub-images using a region-Based Segmentation Technique, and (2) edge detection using a morphological gradient Technique. Classifying the exudates from non-exudates was carried out using rule-Based classification. Using a clinician reference (ground truth), the proposed method was validated, in terms of pixels, with overall sensitivity of 93.1%. Speed and performance of the proposed method show that it is more reproducible than the manual method.

Anthony Cheung - One of the best experts on this subject based on the ideXlab platform.

  • Arabic optical character recognition system using recognition-Based Segmentation
    Pattern Recognition, 2001
    Co-Authors: Anthony Cheung, Mohammed Bennamoun, Neil W. Bergmann
    Abstract:

    Optical character recognition (OC) systems improve human-machine interaction and are widely used in many areas. The recognition of cursive scripts is a difficult task as their Segmentation suffers from serious problems. This paper proposes an Arabic OCR system, which uses a recognition-Based Segmentation Technique to overcome the classical Segmentation problems. A newly developed Arabic word Segmentation algorithm is also introduced to separate horizontally overlapping Arabic words/subwords. There is also a feedback loop to control the combination of character fragments for recognition. The system was implemented and the results show a 90% recognition accuracy with a 20 chars/s recognition rate.

Hermann Kaufmann - One of the best experts on this subject based on the ideXlab platform.