scholarly journals A NOVEL APPROACH TOWARDS ONLINE DEVNAGARI HANDWRITTEN WORD RECOGNITION BASED ON ROBUST FEATURE EXTRACTION METHOD AND FFNN CLASSIFIER.

2017 ◽  
Vol 5 (8) ◽  
pp. 764-776
Author(s):  
Saniya Ansari ◽  
◽  
Bhavani S ◽  
Udaysingh Sutar ◽  
◽  
...  
Keyword(s):  
Feature Extraction ◽  
Word Recognition ◽  
Extraction Method ◽  
Feature Extraction Method ◽  
Novel Approach ◽  
Robust Feature Extraction ◽  
Handwritten Word Recognition

scholarly journals Spatial Domain-Based Nonlinear Residual Feature Extraction for Identification of Image Operations

2020 ◽  
Vol 10 (16) ◽  
pp. 5582
Author(s):  
Xiaochen Yuan ◽  
Tian Huang
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Extraction Method ◽  
Spatial Domain ◽  
Feature Extraction Method ◽  
Novel Approach ◽  
Image Postprocessing ◽  
Learning Technique ◽  
Residual Values

In this paper, a novel approach that uses a deep learning technique is proposed to detect and identify a variety of image operations. First, we propose the spatial domain-based nonlinear residual (SDNR) feature extraction method by constructing residual values from locally supported filters in the spatial domain. By applying minimum and maximum operators, diversity and nonlinearity are introduced; moreover, this construction brings nonsymmetry to the distribution of SDNR samples. Then, we propose applying a deep learning technique to the extracted SDNR features to detect and classify a variety of image operations. Many experiments have been conducted to verify the performance of the proposed approach, and the results indicate that the proposed method performs well in detecting and identifying the various common image postprocessing operations. Furthermore, comparisons between the proposed approach and the existing methods show the superiority of the proposed approach.

scholarly journals Recites fidelity detection system of al-Kautsar verse based on words using mel frequency cepstrum coefficients and cosine similarity

2019 ◽  
Vol 8 (1) ◽  
pp. 27-35
Author(s):  
Jans Hendry ◽  
Aditya Rachman ◽  
Dodi Zulherman
Keyword(s):  
Feature Extraction ◽  
Word Recognition ◽  
Test Data ◽  
Extraction Method ◽  
Detection System ◽  
Test Word ◽  
Word Segmentation ◽  
Cosine Similarity ◽  
Feature Extraction Method ◽  
Similarity Method

In this study, a system has been developed to help detect the accuracy of the reading of the Koran in the Surah Al-Kautsar based on the accuracy of the number and pronunciation of words in one complete surah. This system is very dependent on the accuracy of word segmentation based on envelope signals. The feature extraction method used was Mel Frequency Cepstrum Coefficients (MFCC), while the Cosine Similarity method was used to detect the accuracy of the reading. From 60 data, 30 data were used for training, while the rest were for testing. From each of the 30 training and test data, 15 data were correct readings, and 15 other data were incorrect readings. System accuracy was measured by word-for-word recognition, which results in 100 % of recall and 98.96 % of precision for the training word data, and 100 % of recall and 99.65 % of precision for the test word data. For the overall reading of the surah, there were 15 correct readings and 14 incorrect readings that were recognized correctly.

scholarly journals A P-Norm Robust Feature Extraction Method for Identifying Differentially Expressed Genes

PLoS ONE ◽  
2015 ◽  
Vol 10 (7) ◽  
pp. e0133124 ◽  
Author(s):  
Jian Liu ◽  
Jin-Xing Liu ◽  
Ying-Lian Gao ◽  
Xiang-Zhen Kong ◽  
Xue-Song Wang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Differentially Expressed Genes ◽  
Extraction Method ◽  
Differentially Expressed ◽  
Feature Extraction Method ◽  
Robust Feature Extraction

scholarly journals A Robust Feature Extraction Method for Real-Time Speech Recognition System on a Raspberry Pi 3 Board

2019 ◽  
Vol 9 (2) ◽  
pp. 4066-4070 ◽  
Author(s):  
A. Mnassri ◽  
M. Bennasr ◽  
C. Adnane
Keyword(s):  
Feature Extraction ◽  
Speech Recognition ◽  
Real Time ◽  
Extraction Method ◽  
Recognition System ◽  
Raspberry Pi ◽  
Discrete Wavelet ◽  
Speech Recognition System ◽  
Feature Extraction Method ◽  
Robust Feature Extraction

The development of a real-time automatic speech recognition system (ASR) better adapted to environmental variabilities, such as noisy surroundings, speaker variations and accents has become a high priority. Robustness is required, and it can be performed at the feature extraction stage which avoids the need for other pre-processing steps. In this paper, a new robust feature extraction method for real-time ASR system is presented. A combination of Mel-frequency cepstral coefficients (MFCC) and discrete wavelet transform (DWT) is proposed. This hybrid system can conserve more extracted speech features which tend to be invariant to noise. The main idea is to extract MFCC features by denoising the obtained coefficients in the wavelet domain by using a median filter (MF). The proposed system has been implemented on Raspberry Pi 3 which is a suitable platform for real-time requirements. The experiments showed a high recognition rate (100%) in clean environment and satisfying results (ranging from 80% to 100%) in noisy environments at different signal to noise ratios (SNRs).

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