Address Recognition System for Postal Service Using Neural Networks

Abstract

While most of the Indian industries are in the process of automation, it is a bitter truth that the Indian Postal System is still using manual intervention for its mail sorting and processing. Although for postal automation there are many pieces of work towards street name recognition in non-Indian languages, to the best of our knowledge there is no work on street name recognition in Indian languages. The Automatic Mail Processor (AMP), which we have designed, scans a mail and interprets the imperative fields of the destination address such as the Pin Code, City name, Locality name and the Street name. The interpreted address is subsequently converted into a QR code. The code is reprinted onto the mail which can be read by a low-cost machine. By converting the destination address into a barcode, all of the future sorting processes can be accomplished by using a mechanical machine sorter, which can sort the mails according to the barcode present on them. We used two main approaches to accomplish this task: classifying words directly and character segmentation. For the former, we use Convolutional Neural Network (CNN) with various architectures to train a model that can precisely classify words. We then pass the segmented characters to a R ecurrent Neural Network (RNN) for classification and then reconstruct each word according to the results of classification and segmentation.