Handwritten character recognition with stable and adaptable convolutional neural network

Abstract

Handwritten character recognition is widely used in mail collection and classification, note sorting and filing, financial data identification and analysis, etc. Current machine learning methods for handwritten character recognition mainly include Random Forest (RF) and Support Vector Machine (SVM). However, the performance of these methods compromises a lot due to high noise, non-linearity, and unobvious feature values in handwritten character images. This paper presents a stable and adaptable Convolutional Neural Network (CNN) to recognize handwritten characters. First, this paper preprocesses the images by noise reduction, one-hot encoding, and contrast enhancement to increase the recognizability. Then, this paper combines the structures of several CNN classic models to construct a CNN model with strong adaptability and high stability. This CNN model loops through a combination of a convolutional layer, a pooling layer, and a Dropout layer, which realizes the full connection of local features and reduces the amounts of parameters and data complexity. The Dense layers map the flattened image data to the output space by extracting the correlation between the features. Each layer in this CNN combines with Relu-based activation functions to perform nonlinear mapping, alleviating problems such as gradient disappearance and overfitting. The proposed CNN is compared with RF and SVM on the English Handwritten Characters dataset with accuracy and confusion matrix. The recognition accuracies of RF, SVM and CNN are 0.90, 0.92 and 0.96, respectively. RF and SVM confuse multiple characters, while CNN only generates a small number of confusion on the characters G and S. In the test with actual handwritten characters as samples, this CNN also shows higher recognition accuracy and fewer confused characters than RF and SVM. In addition, this paper compares and evaluates this CNN with other representative CNN models. The experimental result shows that the proposed CNN has a greater advantage in the balance of fitting speed and recognition accuracy.