# A contextual analysis of multi-layer perceptron models in classifying hand-written digits and letters: limited resources

arXiv: https://arxiv.org/abs/2107.01782 · HTML: https://mlsanity.com/paper/2107.01782/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 24 references that are in the map.
- First posted on arXiv 5 Jul 2021.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [Feature Learning in Deep Neural Networks - Studies on Speech Recognition Tasks](https://mlsanity.com/paper/1301.3605/) (2013)
- [Assessing four Neural Networks on Handwritten Digit Recognition Dataset (MNIST)](https://mlsanity.com/paper/1811.08278/) (2018)
- [Online and Adaptive Pseudoinverse Solutions for ELM Weights](https://mlsanity.com/paper/1405.7777/) (2014)
- [High-Accuracy Inference in Neuromorphic Circuits using Hardware-Aware Training](https://mlsanity.com/paper/1809.04982/) (2018)

## Abstract (arXiv)

Classifying hand-written digits and letters has taken a big leap with the introduction of ConvNets. However, on very constrained hardware the time necessary to train such models would be high. Our main contribution is twofold. First, we extensively test an end-to-end vanilla neural network (MLP) approach in pure numpy without any pre-processing or feature extraction done beforehand. Second, we show that basic data mining operations can significantly improve the performance of the models in terms of computational time, without sacrificing much accuracy. We illustrate our claims on a simpler variant of the Extended MNIST dataset, called Balanced EMNIST dataset. Our experiments show that, without any data mining, we get increased generalization performance when using more hidden layers and regularization techniques, the best model achieving 84.83% accuracy on a test dataset. Using dimensionality reduction done by PCA we were able to increase that figure to 85.08% with only 10% of the original feature space, reducing the memory size needed by 64%. Finally, adding methods to remove possibly harmful training samples like deviation from the mean helped us to still achieve over 84% test accuracy but with only 32.8% of the original memory size for the training set. This compares favorably to the majority of literature results obtained through similar architectures. Although this approach gets outshined by state-of-the-art models, it does scale to some (AlexNet, VGGNet) trained on 50% of the same dataset.