Handwriting recognition is one of the most significant problems in pattern recognition,many studies have been proposed to improve this recognition of handwritten text for different languages.Yet,Fewer studies have bee...Handwriting recognition is one of the most significant problems in pattern recognition,many studies have been proposed to improve this recognition of handwritten text for different languages.Yet,Fewer studies have been done for the Arabic language and the processing of its texts remains a particularly distinctive problem due to the variability of writing styles and the nature of Arabic scripts compared to other scripts.The present paper suggests a feature extraction technique for offlineArabic handwriting recognition.A handwriting recognition system for Arabic words using a few important structural features and based on a Radial Basis Function(RBF)neural networks is proposed.The methods of feature extraction are central to achieve high recognition performance.The proposed methodology relies on a feature extraction technique based on many structural characteristics extracted from the word skeleton(subwords,diacritics,loops,ascenders,and descenders).In order to reach our purpose,we built our own word database and the proposed system has been successfully tested on a handwriting database of Algerian city names(wilayas).Finally,a simple classifier based on the radial basis function neural network is presented to recognize certain words to verify the reliability of the proposed feature extraction.The experiments on some images of the benchmark IFN/ENIT database show that the proposed system improves recognition and the results obtained are indicative of the efficiency of our technique.展开更多
The purpose of this work is to associate the channel encoder called ‘trellis-coded modulation with Ungerboeck-Gray mapping’ (TCM-UGM) to ‘space–time block code’ (STBC), in order to study its performance to correc...The purpose of this work is to associate the channel encoder called ‘trellis-coded modulation with Ungerboeck-Gray mapping’ (TCM-UGM) to ‘space–time block code’ (STBC), in order to study its performance to correct the transmission errors of a JPEG image. The performance of the proposed scheme is evaluated in senses of bit error rate (BER), frame error rate (FER) and peak signal-to-noise ratio (PSNR) of the reconstructed image. Compared to the association TCM/STBC for a throughput of 2 bits/s/Hz, TCM-UGM/STBC permits to obtain a PSNR gain up to 2 dB.展开更多
文摘Handwriting recognition is one of the most significant problems in pattern recognition,many studies have been proposed to improve this recognition of handwritten text for different languages.Yet,Fewer studies have been done for the Arabic language and the processing of its texts remains a particularly distinctive problem due to the variability of writing styles and the nature of Arabic scripts compared to other scripts.The present paper suggests a feature extraction technique for offlineArabic handwriting recognition.A handwriting recognition system for Arabic words using a few important structural features and based on a Radial Basis Function(RBF)neural networks is proposed.The methods of feature extraction are central to achieve high recognition performance.The proposed methodology relies on a feature extraction technique based on many structural characteristics extracted from the word skeleton(subwords,diacritics,loops,ascenders,and descenders).In order to reach our purpose,we built our own word database and the proposed system has been successfully tested on a handwriting database of Algerian city names(wilayas).Finally,a simple classifier based on the radial basis function neural network is presented to recognize certain words to verify the reliability of the proposed feature extraction.The experiments on some images of the benchmark IFN/ENIT database show that the proposed system improves recognition and the results obtained are indicative of the efficiency of our technique.
文摘The purpose of this work is to associate the channel encoder called ‘trellis-coded modulation with Ungerboeck-Gray mapping’ (TCM-UGM) to ‘space–time block code’ (STBC), in order to study its performance to correct the transmission errors of a JPEG image. The performance of the proposed scheme is evaluated in senses of bit error rate (BER), frame error rate (FER) and peak signal-to-noise ratio (PSNR) of the reconstructed image. Compared to the association TCM/STBC for a throughput of 2 bits/s/Hz, TCM-UGM/STBC permits to obtain a PSNR gain up to 2 dB.
