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Classify Handwritten Digits Using CNN and Random Forest
Tiny GU
Department of Computer Science
University of New Hampshire
Durham, NH 03824 USA
tg1034×wildcats.UNH.abstract
Natural handwriting is
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How to fill out classify handwritten digtits using
How to fill out classify handwritten digtits using
01
Step 1: Start by obtaining a dataset of handwritten digits.
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Step 2: Preprocess the dataset by normalizing the images and converting them to a suitable format.
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Step 3: Split the dataset into training and testing sets.
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Step 4: Build a classification model such as a neural network or a support vector machine.
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Step 5: Train the model using the training set.
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Step 6: Evaluate the trained model using the testing set to measure its performance.
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Step 7: Once the model is trained and evaluated, it can be used to classify new handwritten digits.
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Step 8: Provide the new handwritten digit image as input to the model and obtain the predicted class.
Who needs classify handwritten digtits using?
01
Researchers working on pattern recognition and computer vision.
02
Organizations involved in digit recognition for postal services or document processing.
03
Developers creating applications that require automatic recognition of handwritten digits.
04
Machine learning enthusiasts interested in exploring classification algorithms.
05
Students and educators studying or teaching machine learning and image processing.
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