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https://github.com/manoharvit/digits-recognition-system-using-machinelearning
The approach and dataset determines the system's accuracy and efficiency. For handwritten digit recognition, this paper provides a reasonable overview of machine and deep learning techniques such as SVM (Support Vector Machine), CNN (Convolutional Neural Network), and KNN (K Nearest Neighbor). It also shows which algorithm is the most effective at doing digit recognition. It also provides a comparison among different algorithms based on their accuracy, so that the most accurate method with the minimum errors can be used in distinct handwritten digit recognition applications.
https://github.com/manoharvit/digits-recognition-system-using-machinelearning
Last synced: about 2 months ago
JSON representation
The approach and dataset determines the system's accuracy and efficiency. For handwritten digit recognition, this paper provides a reasonable overview of machine and deep learning techniques such as SVM (Support Vector Machine), CNN (Convolutional Neural Network), and KNN (K Nearest Neighbor). It also shows which algorithm is the most effective at doing digit recognition. It also provides a comparison among different algorithms based on their accuracy, so that the most accurate method with the minimum errors can be used in distinct handwritten digit recognition applications.
- Host: GitHub
- URL: https://github.com/manoharvit/digits-recognition-system-using-machinelearning
- Owner: ManoharVit
- Created: 2022-01-20T09:16:13.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2022-01-20T09:17:55.000Z (almost 3 years ago)
- Last Synced: 2023-05-19T13:25:27.515Z (over 1 year ago)
- Language: Jupyter Notebook
- Size: 114 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0