Postal Automation System in Gurmukhi Script using Deep Learning

Nowadays in the era of automation, the postal automation system is one of the major research areas. Developing a postal automation system for a nation like India is much troublesome than other nations because of India’s multi-script and multi-lingual behavior. This proposed work will be helpful in the postal automation of district names of Punjab (state) written in Gurmukhi script, which is the official language of the state in North India. For this, a holistic approach i.e. a segmentation-free technique has been used with the help of Convolutional Neural Network (CNN) and Deep learning (DL). For the purpose of recognition, a database of 22[Formula: see text]000 images (samples) which are handwritten in Gurmukhi script for all the 22 districts of Punjab is prepared. Each sample is written two times by 500 different writers generating 1000 samples for each district name. Two CNN models are proposed which are named as ConvNetGuru and ConvNetGuruMod for the purpose of recognition. Maximum validation accuracy achieved by ConvNetGuru is 90% and ConvNetGuruMod is 98%.

Postal Address Identification and Sorting

In this fast-moving world, a normal man can take considerable time to find a postal card in a bunch of postcards with significant issues like unclear handwriting, having trouble recognizing some uncommon or ambiguous names. Also, in postal offices or industries, it negatively impacts the efficiency of the postal system. I am making a system for Indian postal automation based on recognizing pin-code on the postcard. In India, there are multiple languages were speak. Indian postcards are mainly written in three languages the state's official language, English, and Devanagari language. In India, more than 50% of people write Pincode digits in either English or Devanagari language, so I am making such a system that sorts both English and Devanagari language postcards. Moreover, the system is mature enough to recognize handwritten as well as printed digits. As a result, the system gets an accuracy of 92.59% on the English language postcards, 90% accuracy on the Devanagari language postcards e and the digit recognition model gives accuracy 99.23% Devanagari numerals and 99.43% accuracy on English numerals.

Address Recognition System for Postal Service Using Neural Networks

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.