Research and application of constructing image recognition models for archival handwriting

Abstract

Traditional file management mainly relies on manual processing, but with the development of artificial intelligence, the convolutional neural network (CNN) model has been widely used in the acquisition, processing, recognition, and conversion of handwritten text, which improves the efficiency and achieves accurate and fast information processing. In order to achieve high-precision handwritten digit recognition, this study proposes a high-performance model based on convolutional neural networks, which extracts features through a convolutional layer, under-samples using a maximum pooling layer to retain important features, and further extracts and classifies the features through a fully connected layer, and ultimately outputs the classified probability distributions using softmax functions. We compare the three optimization algorithms: experimental results show that the RMSprop optimizer has a training accuracy of more than 99% on the MNIST dataset, with a loss rate close to zero, whereas SGD and Adam, although slightly inferior in terms of performance, still have good recognition results, which verifies the superiority of CNNs combined with the RMSprop optimizer in the handwritten digit recognition task.