scholarly journals An Efficient Method for Handwritten Kannada Digit Recognition based on PCA and SVM Classifier

2021 ◽  
Vol 9 (35) ◽  
pp. 169-182
Author(s):  
Ramesh G ◽  
Prasanna G B ◽  
Santosh V Bhat ◽  
Chandrashekar Naik ◽  
Champa H N
Keyword(s):  
Efficient Method ◽  
Svm Classifier ◽  
Digit Recognition

scholarly journals Persian Handwritten Digit Recognition Using Combination of Convolutional Neural Network and Support Vector Machine Methods

2020 ◽  
Vol 17 (4) ◽  
pp. 572-578
Author(s):  
Mohammad Parseh ◽  
Mohammad Rahmanimanesh ◽  
Parviz Keshavarzi
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Recognition Rate ◽  
Support Vector ◽  
Svm Classifier ◽  
Handwritten Digit Recognition ◽  
Digit Recognition ◽  
Handwritten Digit

Persian handwritten digit recognition is one of the important topics of image processing which significantly considered by researchers due to its many applications. The most important challenges in Persian handwritten digit recognition is the existence of various patterns in Persian digit writing that makes the feature extraction step to be more complicated.Since the handcraft feature extraction methods are complicated processes and their performance level are not stable, most of the recent studies have concentrated on proposing a suitable method for automatic feature extraction. In this paper, an automatic method based on machine learning is proposed for high-level feature extraction from Persian digit images by using Convolutional Neural Network (CNN). After that, a non-linear multi-class Support Vector Machine (SVM) classifier is used for data classification instead of fully connected layer in final layer of CNN. The proposed method has been applied to HODA dataset and obtained 99.56% of recognition rate. Experimental results are comparable with previous state-of-the-art methods

scholarly journals Uttered Kurdish digit recognition system

2019 ◽  
Vol 6 (2) ◽  
pp. 78-85 ◽  
Author(s):  
Saman Muhammad Omer ◽  
Jihad Anwar Qadir ◽  
Zrar Khalid Abdul
Keyword(s):  
Speech Recognition ◽  
Predictive Coding ◽  
Recognition System ◽  
Svm Classifier ◽  
Linear Predictive Coding ◽  
Formant Frequencies ◽  
Digit Recognition ◽  
Proposed Model ◽  
Machine Readable ◽  
Mel Frequency Cepstral Coefficient

Speech recognition is a crucial subject in human computer interaction area. The ability of a machine to recognize words and phrases in spoken language is speech recognition and then convert them to a machine-readable format. Digit recognition is a part of the speech recognition system. In this paper, three spectral based features including Mel Frequency Cepstral Coefficient (MFCC), Linear predictive coding (LPC) and formant frequencies are proposed to classify ten Kurdish uttered digits (0-9). The features are extracted from entire speech signal, and feed a pairwise SVM classifier. Experiments including each individual feature and different forms of fusion are conducted and the results are shown. The fusion of the features significantly improves the result and shows that the different features carry complementary information. The proposed model is experimented on the dataset that have been collected in Kurdistan. Key words: Speech recognition, MFCC, LPC, Formant frequencies, uttered digits, SVM

scholarly journals Comparison of convolutional neural network and bag of features for multi-font digit recognition

2019 ◽  
Vol 15 (3) ◽  
pp. 1322
Author(s):  
Nasibah Husna Mohd Kadir ◽  
Sharifah Nur Syafiqah Mohd Nur Hidayah ◽  
Norasiah Mohammad ◽  
Zaidah Ibrahim
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Character Recognition ◽  
Recognition Accuracy ◽  
Recognition Performance ◽  
Support Vector ◽  
Svm Classifier ◽  
Learning Method ◽  
Digit Recognition ◽  
Bag Of Features

<span>This paper evaluates the recognition performance of Convolutional Neural Network (CNN) and Bag of Features (BoF) for multiple font digit recognition. Font digit recognition is part of character recognition that is used to translate images from many document-input tasks such as handwritten, typewritten and printed text.  BoF is a popular machine learning method while CNN is a popular deep learning method.  Experiments were performed by applying BoF with Speeded-up Robust Feature (SURF) and Support Vector Machine (SVM) classifier and compared with CNN on Chars74K dataset. The recognition accuracy produced by BoF is just slightly lower than CNN where the accuracy of CNN is 0.96 while the accuracy of BoF is 0.94.</span>

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