Handcrafted and deep learning-based features for handwritten digit recognition

Abstract

Handwritten digit recognition is a classic problem in the field of pattern recognition and machine learning. The goal is to develop algorithms that can accurately identify and classify digits from handwritten images. Handcrafted and deep learning-based features are two approaches used to extract relevant information from these images for the recognition task. Handcrafted features refer to manually designed and selected characteristics or attributes of the input data that are believed to be relevant for the recognition task. In the context of handwritten digit recognition, handcrafted features can include measures such as the aspect

ratio of the digits, the number of loops, the curvature of specific strokes, and other domain- specific characteristics.

Both approaches have been explored for handwritten digit recognition,. Traditional machine learning methods often rely on handcrafted features, while deep learning methods, especially CNNs, have shown remarkable success in learning features directly from raw pixel values, eliminating the need for explicit feature engineering.