Classification of handwritten names of cities using various deep learning models

Abstract

The digitized text of handwriting would conduce to automate the business processes of many companies, simplifying the work of human being. For example, our state postal service does not have an automated mail processing system that recognizes handwritten addresses on an envelope. Each incoming correspondence is registered in the system by the operator. Automation of this business process on registering post mailing will significantly reduce expenses of postal service on mail delivery.There are two main approaches to handwriting recognition, namely hidden Markov models (HMM) and artificial neural networks (ANN). The methods proposed in this article are based on ANN. The first model is based on deep convolutional neural networks (DCNN) [1] for feature extraction and a fully connected multilayer perceptron (fully connected MLP) for word classification. The next model under consideration, called SimpleHTR, proposed by Harald Scheidl [2], has layers of a convolutional neural network (CNN) and layers of a recurrent neural network (RNN) for disseminating information through an image. Finally, the Connectionist Temporal Classification (CTC) decoding algorithm is executed, which adduces the text to the final version.Models were learned on the dataset of handwritten city names from Cyrillic words. 21,000 images were collected (42 classes of 500 handwriting samples). To increase the data set for training, 207,438 images from available samples were generated.As a result, two approaches for handwriting recognition were analyzed and the SimpleHTR model showed the best results over all.