MapReduce-based deep learning with handwritten digit recognition case study

2016 ◽  
Author(s):  
Nada Basit ◽  
Yutong Zhang ◽  
Hao Wu ◽  
Haoran Liu ◽  
Jieming Bin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Handwritten Digit Recognition ◽  
Digit Recognition ◽  
Handwritten Digit

scholarly journals Analysis of machine learning algorithms for character recognition: a case study on handwritten digit recognition

2021 ◽  
Vol 21 (1) ◽  
pp. 574
Author(s):  
Owais Mujtaba Khandy ◽  
Samad Dadvandipour
Keyword(s):  
Machine Learning ◽  
Random Forests ◽  
Character Recognition ◽  
Machine Learning Algorithms ◽  
Maximum Efficiency ◽  
Handwritten Digit Recognition ◽  
Digit Recognition ◽  
Handwritten Digit ◽  
Work Done

<p><span>This paper covers the work done in handwritten digit recognition and the various classifiers that have been developed. Methods like MLP, SVM, Bayesian networks, and Random forests were discussed with their accuracy and are empirically evaluated. Boosted LetNet 4, an ensemble of various classifiers, has shown maximum efficiency among these methods. </span></p>

scholarly journals Handwritten Digit Recognition using Convolutional Neural Network in Python with Tensorflow and Observe the Variation of Accuracies for Various Hidden Layers

2019 ◽  
Author(s):  
Fathma Siddique ◽  
Shadman Sakib ◽  
Md. Abu Bakr Siddique
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Handwritten Digit Recognition ◽  
Diverse Range ◽  
Digit Recognition ◽  
Handwritten Digit ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In recent times, with the increase of Artificial Neural Network (ANN), deep learning has brought a dramatic twist in the field of machine learning by making it more Artificial Intelligence (AI). Deep learning is used remarkably used in vast ranges of fields because of its diverse range of applications such as surveillance, health, medicine, sports, robotics, drones etc. In deep learning, Convolutional Neural Network (CNN) is at the center of spectacular advances that mixes Artificial Neural Network (ANN) and up to date deep learning strategies. It has been used broadly in pattern recognition, sentence classification, speech recognition, face recognition, text categorization, document analysis, scene, and handwritten digit recognition. The goal of this paper is to observe the variation of accuracies of CNN to classify handwritten digits using various numbers of hidden layer and epochs and to make the comparison between the accuracies. For this performance evaluation of CNN, we performed our experiment using Modified National Institute of Standards and Technology (MNIST) dataset. Further, the network is trained using stochastic gradient descent and the backpropagation algorithm.

Handwritten Digit Recognition Using Deep Learning

2021 ◽  
pp. 153-158
Author(s):  
Bhagyashree P M ◽  
L K Likhitha ◽  
D S Rajesh
Keyword(s):  
Deep Learning ◽  
Learning Strategies ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Handwriting Recognition ◽  
Handwritten Digit Recognition ◽  
Digit Recognition ◽  
Handwritten Digit ◽  
Overall Performance ◽  
Similar Accuracy

Traditional systems of handwritten Digit Recognition have depended on handcrafted functions and a massive amount of previous knowledge. Training an Optical character recognition (OCR) system primarily based totally on those stipulations is a hard task. Research in the handwriting recognition subject is centered on deep learning strategies and has accomplished breakthrough overall performance in the previous couple of years. Convolutional neural networks (CNNs) are very powerful in perceiving the structure of handwritten digits in ways that assist in automated extraction of features and make CNN the most appropriate technique for solving handwriting recognition problems. Here, our goal is to attain similar accuracy through the use of a pure CNN structure.CNN structure is proposed to be able to attain accuracy even higher than that of ensemble architectures, alongside decreased operational complexity and price. The proposed method gives 99.87 accuracy for real-world handwritten digit prediction with less than 0.1 % loss on training with 60000 digits while 10000 under validation.

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