# Handwritten Digit Recognition: An Ensemble-Based Approach for Superior Performance

arXiv: https://arxiv.org/abs/2503.06104 · HTML: https://mlsanity.com/paper/2503.06104/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 26 references that are in the map.
- First posted on arXiv 8 Mar 2025.

## Builds on (most cited first, 5 of 5)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)](https://mlsanity.com/paper/2001.00139/) (2020)
- [DropSample: A New Training Method to Enhance Deep Convolutional Neural Networks for Large-Scale Unconstrained Handwritten Chinese Character Recognition](https://mlsanity.com/paper/1505.05354/) (2015)
- [Recurrent Connections Aid Occluded Object Recognition by Discounting Occluders](https://mlsanity.com/paper/1907.08831/) (2019)
- [Implicit recurrent networks: A novel approach to stationary input processing with recurrent neural networks in deep learning](https://mlsanity.com/paper/2010.10564/) (2020)

## Built on by (most cited first, 1 of 1)

- [Designing Compact Neural Architectures via Neuron Gating and Mixed Activation](https://mlsanity.com/paper/2608.14443/) (2026)

## Abstract (arXiv)

Handwritten digit recognition remains a fundamental challenge in computer vision, with applications ranging from postal code reading to document digitization. This paper presents an ensemble-based approach that combines Convolutional Neural Networks (CNNs) with traditional machine learning techniques to improve recognition accuracy and robustness. We evaluate our method on the MNIST dataset, comprising 70,000 handwritten digit images. Our hybrid model, which uses CNNs for feature extraction and Support Vector Machines (SVMs) for classification, achieves an accuracy of 99.30%. We also explore the effectiveness of data augmentation and various ensemble techniques in enhancing model performance. Our results demonstrate that this approach not only achieves high accuracy but also shows improved generalization across diverse handwriting styles. The findings contribute to the development of more reliable handwritten digit recognition systems and highlight the potential of combining deep learning with traditional machine learning methods in pattern recognition tasks.