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Cloud-based efficient scheme for handwritten digit recognition
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DOI：10.1007/s11042-020-09494-1.png)
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Abstract
En 中文
Handwritten character recognition has been acknowledged and achieved more prominent attention in pattern recognition research community due to enormous applications & vagueness in application methods, while cloud computing delivers appropriate, on-demand access of network to a joint tarn of configurable computing resource & digital devices. Principally two steps, feature extraction & character recognition, are required for Handwritten Digit Recognition (HDR), which are primarily based on some classification algorithms. Previous studies show the nonexistence of higher precision and truncated computational swiftness for HDR procedure. The projected research aimed to make the trail towards digitalization clearer by providing high accuracy and faster cloud-based computational for handwritten digits recognition. The current study utilized a cloud-based neural network (CNN) as a classifier, suitable parameters of dataset MNIST for testing and training purposes as a framework called DL4J for cloud-based handwritten digit recognition. The said system magnificently managed to obtained precision up to 99.41%, which is higher than previously projected systems. Additionally, the proposed method decreases cost and computational time significantly as using cloud-based architecture for testing and training; as a result, the algorithm becomes more efficient.
Keywords:
Convolutional Neural networks
Cloud computing
Handwritten digit recognition
Deep learning 4 J
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