User acceptance of handwritten recognition accuracy

The recognition of handwritten Bangla digit is providing significant progress on optical character recognition (OCR). It is a very critical task due to the similar pattern and alignment of handwriting digits. With the progress of modern research on optical character recognition, it is reducing the complexity of the classification task by several methods, a few problems encounter during recognition and wait to be solved with simpler methods. The modern emerging field of artificial intelligence is the Deep Neural Network, which promises a solid solution to these few handwritten recognition problems. This paper proposed a fine regulated deep neural network (FRDNN) for the handwritten numeric character recognition problem that uses convolutional neural network (CNN) models with regularization parameters which makes the model generalized by preventing the overfitting. This paper applied Traditional Deep Neural Network (TDNN) and Fine regulated deep neural network (FRDNN) models with a similar layer experienced on BanglaLekha-Isolated databases and the classification accuracies for the two models were 96.25% and 96.99%, respectively over 100 epochs. The network performance of the FRDNN model on the BanglaLekha-Isolated digit dataset was more robust and accurate than the TDNN model and depend on experimentation. Our proposed method is obtained a good recognition accuracy compared with other existing available methods.

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Effects of recognition accuracy and vocabulary size of a speech recognition system on task performance and user acceptance

Applied Ergonomics ◽

10.1016/0003-6870(91)90427-j ◽

1991 ◽

Vol 22 (5) ◽

pp. 352

Keyword(s):

Speech Recognition ◽

Task Performance ◽

Recognition Accuracy ◽

Recognition System ◽

User Acceptance ◽

Speech Recognition System ◽

Vocabulary Size

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Effects of Recognition Accuracy and Vocabulary Size of a Speech Recognition System on Task Performance and User Acceptance

Human Factors The Journal of the Human Factors and Ergonomics Society ◽

10.1177/001872089003200206 ◽

1990 ◽

Vol 32 (2) ◽

pp. 183-196 ◽

Cited By ~ 16

Author(s):

Sherry Perdue Casali ◽

Beverly H. Williges ◽

Robert D. Dryden

Keyword(s):

Speech Recognition ◽

Task Performance ◽

Recognition Accuracy ◽

Recognition System ◽

User Acceptance ◽

Speech Recognition System ◽

Vocabulary Size

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Development of English Handwritten Recognition Using Deep Neural Network

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v10.i2.pp562-568 ◽

2018 ◽

Vol 10 (2) ◽

pp. 562 ◽

Cited By ~ 5

Author(s):

Teddy Surya Gunawan ◽

Ahmad Fakhrur Razi Mohd Noor ◽

Mira Kartiwi

Keyword(s):

Neural Network ◽

Deep Neural Network ◽

Recognition Accuracy ◽

Recognition Rate ◽

Weighted Average ◽

Recognition System ◽

High Accuracy ◽

Performance Comparison ◽

Average Recognition Rate ◽

Handwritten Recognition

Due to the advanced in GPU and CPU, in recent years, Deep Neural Network (DNN) becomes popular to be utilized both as feature extraction and classifier. This paper aims to develop offline handwritten recognition system using DNN. First, two popular English digits and letters database, i.e. MNIST and EMNIST, were selected to provide dataset for training and testing phase of DNN. Altogether, there are 10 digits [0-9] and 52 letters [a-z, A-Z]. The proposed DNN used stacked two autoencoder layers and one softmax layer. Recognition accuracy for English digits and letters is 97.7% and 88.8%, respectively. Performance comparison with other structure of neural networks revealed that the weighted average recognition rate for patternnet, feedforwardnet, and proposed DNN were 80.3%, 68.3%, and 90.4%, respectively. It shows that our proposed system is able to recognize handwritten English digits and letters with high accuracy.

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Pengenalan Karakter Tulisan Tangan Dengan K-Support Vector Nearest Neighbor

IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) ◽

10.22146/ijeis.38729 ◽

2019 ◽

Vol 9 (1) ◽

pp. 33

Author(s):

Aditya Surya Wijaya ◽

Nurul Chamidah ◽

Mayanda Mega Santoni

Keyword(s):

Character Recognition ◽

Nearest Neighbor ◽

Recognition Accuracy ◽

Training Data ◽

Support Vector ◽

K Nearest Neighbor ◽

Zone Method ◽

Handwritten Character ◽

Testing Data ◽

Handwritten Recognition

Handwritten characters are difficult to be recognized by machine because people had various own writing style. This research recognizes handwritten character pattern of numbers and alphabet using K-Nearest Neighbour (KNN) algorithm. Handwritten recognition process is worked by preprocessing handwritten image, segmentation to obtain separate single characters, feature extraction, and classification. Features extraction is done by utilizing Zone method that will be used for classification by splitting this features data to training data and testing data. Training data from extracted features reduced by K-Support Vector Nearest Neighbor (K-SVNN) and for recognizing handwritten pattern from testing data, we used K-Nearest Neighbor (KNN). Testing result shows that reducing training data using K-SVNN able to improve handwritten character recognition accuracy.

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Recognition accuracy and user acceptance of pen interfaces

Proceedings of the SIGCHI conference on Human factors in computing systems - CHI '95 ◽

10.1145/223904.223972 ◽

1995 ◽

Cited By ~ 33

Author(s):

Clive Frankish ◽

Richard Hull ◽

Pam Morgan

Keyword(s):

Recognition Accuracy ◽

User Acceptance

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Influence of the Acoustic Reflex on Vowel Recognition

Journal of Speech Language and Hearing Research ◽

10.1044/jshr.2903.420 ◽

1986 ◽

Vol 29 (3) ◽

pp. 420-424 ◽

Cited By ~ 4

Author(s):

Michael Dorman ◽

Ingrid Cedar ◽

Maureen Hannley ◽

Marjorie Leek ◽

Julie Mapes Lindholm

Keyword(s):

Auditory System ◽

Sound Pressure ◽

Dynamic Range ◽

Recognition Accuracy ◽

Pressure Level ◽

Acoustic Reflex ◽

The Poor ◽

Stapedial Reflex ◽

Vowel Recognition ◽

High Sound

Computer synthesized vowels of 50- and 300-ms duration were presented to normal-hearing listeners at a moderate and high sound pressure level (SPL). Presentation at the high SPL resulted in poor recognition accuracy for vowels of a duration (50 ms) shorter than the latency of the acoustic stapedial reflex. Presentation level had no effect on recognition accuracy for vowels of sufficient duration (300 ms) to elicit the reflex. The poor recognition accuracy for the brief, high intensity vowels was significantly improved when the reflex was preactivated. These results demonstrate the importance of the acoustic reflex in extending the dynamic range of the auditory system for speech recognition.

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