Abstract
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.
Abstract (translated)
手写数字识别是计算机视觉中的一个基本挑战，其应用范围从邮政编码读取到文档数字化。本文提出了一种基于集成的方法，该方法结合了卷积神经网络（CNN）和传统机器学习技术，以提高识别的准确性和鲁棒性。我们在MNIST数据集上评估了我们的方法，该数据集包含70,000张手写数字图像。我们混合模型使用CNN进行特征提取，并用支持向量机(SVM)进行分类，在测试中达到了99.30%的准确率。此外，我们还探讨了数据增强和各种集成技术在提高模型性能方面的有效性。我们的结果表明，该方法不仅实现了高精度，而且对不同书写风格也表现出更好的泛化能力。这些发现为开发更可靠的手写数字识别系统做出了贡献，并突显了结合深度学习与传统机器学习方法在模式识别任务中的潜力。
URL
https://arxiv.org/abs/2503.06104
