Recognizing Cursive Typewritten Text Using Segmentation-Free System
Identifieur interne : 000037 ( Main/Exploration ); précédent : 000036; suivant : 000038Recognizing Cursive Typewritten Text Using Segmentation-Free System
Auteurs : Mohammad S. Khorsheed [Arabie saoudite]Source :
- The Scientific World Journal [ 2356-6140 ] ; 2015.
Abstract
Feature extraction plays an important role in text recognition as it aims to capture essential characteristics of the text image. Feature extraction algorithms widely range between robust and hard to extract features and noise sensitive and easy to extract features. Among those feature types are statistical features which are derived from the statistical distribution of the image pixels. This paper presents a novel method for feature extraction where simple statistical features are extracted from a one-pixel wide window that slides across the text line. The feature set is clustered in the feature space using vector quantization. The feature vector sequence is then injected to a classification engine for training and recognition purposes. The recognition system is applied to a data corpus which includes cursive Arabic text of more than 600 A4-size sheets typewritten in multiple computer-generated fonts. The system performance is compared to a previously published system from the literature with a similar engine but a different feature set.
Url:
DOI: 10.1155/2015/818432
PubMed: 25961075
PubMed Central: 4413039
Affiliations:
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Le document en format XML
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<front><div type="abstract" xml:lang="en"><p>Feature extraction plays an important role in text recognition as it aims to capture essential characteristics of the text image. Feature extraction algorithms widely range between robust and hard to extract features and noise sensitive and easy to extract features. Among those feature types are statistical features which are derived from the statistical distribution of the image pixels. This paper presents a novel method for feature extraction where simple statistical features are extracted from a one-pixel wide window that slides across the text line. The feature set is clustered in the feature space using vector quantization. The feature vector sequence is then injected to a classification engine for training and recognition purposes. The recognition system is applied to a data corpus which includes cursive Arabic text of more than 600 A4-size sheets typewritten in multiple computer-generated fonts. The system performance is compared to a previously published system from the literature with a similar engine but a different feature set.</p>
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