Pruning Distorted Images in MNIST Handwritten Digits
Amarnath R, Vinay Kumar V

TL;DR
This paper introduces a two-stage deep learning method to improve handwritten digit recognition on MNIST by filtering out distorted images, resulting in over 99.5% accuracy.
Contribution
The study presents a novel filtering approach that enhances MNIST digit classification accuracy by removing distorted images before retraining the model.
Findings
Achieved over 99.5% accuracy on MNIST test set.
Filtering distorted images improves model confidence and reduces errors.
Method effectively mitigates issues of underfitting and overfitting.
Abstract
Recognizing handwritten digits is a challenging task primarily due to the diversity of writing styles and the presence of noisy images. The widely used MNIST dataset, which is commonly employed as a benchmark for this task, includes distorted digits with irregular shapes, incomplete strokes, and varying skew in both the training and testing datasets. Consequently, these factors contribute to reduced accuracy in digit recognition. To overcome this challenge, we propose a two-stage deep learning approach. In the first stage, we create a simple neural network to identify distorted digits within the training set. This model serves to detect and filter out such distorted and ambiguous images. In the second stage, we exclude these identified images from the training dataset and proceed to retrain the model using the filtered dataset. This process aims to improve the classification accuracy…
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Taxonomy
TopicsHandwritten Text Recognition Techniques · Advanced Neural Network Applications · Digital Media Forensic Detection
