Digital Recognition Methods Based on Deep Learning

Abstract

In this paper, the K-nearest neighbor algorithm and the convolutional neural network will be used to train the handwritten digit recognition model, respectively. To establish a reasonable model structure, and through the training data, the model can learn to reflect ten different handwritten number features and finally give the probability of predicting number corresponding to the likelihood of each number. Taking the learning process of the handwritten numeral recognition algorithm based on deep learning as a clue, from deep learning to convolutional neural network, from simple to deep, the relevant basic concepts, model construction, and training process of deep learning are learned and understood. Finally, the deep learning framework uses MNIST as the training dataset to train a model with high recognition rate and then combines it with Open CV technology to realize the identification of handwritten numbers. A reasonable model structure is used to accurately identify the handwritten numbers in the test set. The neural network of deep learning is established with TensorFlow to realize the classification and recognition of handwritten numbers. Various deep learning methods such as CNN and KNN are learned and compared to complete the construction of deep learning architecture. The MNIST dataset was preprocessed, features extracted, and identified. The program is to complete the training of neural network and the recognition of numbers in the image, the recognition results of deep learning methods used are counted and analyzed, and the recognition rates of two different methods are compared to find ways to optimize these methods and improve the recognition rate.