Handwritten digit recognition algorithm based on DBN and improved KNN algorithm

Inhalt:
K-nearest neighbor (KNN) algorithm is widely used in handwritten digit recognition technology because it has the characteristics of no data input assumption, high accuracy and insensitivity to outliers, but the space complexity and computational complexity of KNN algorithm are relatively high. The deep belief nets (DBN) deep learning model has excellent classification performance and is widely used in speech recognition, image recognition and other fields. Handwriting recognition technology is a technology derived from machine learning. The primary task of handwritten number recognition technology is to digitize images. This article integrates the DBN deep learning model into the KNN algorithm, and proposes a handwritten number recognition system based on the improved KNN algorithm. The test results show that the performance of the algorithm is significantly better than the traditional KNN algorithm and DBN algorithm.