Benchmark Dataset: Offline Handwritten Gurmukhi City Names for Postal Automation

Abstract

Handwriting recognition delineate the computer’s ability to convert human handwriting into text that can be processed by machine. Postal automation plays a significant role in image processing and pattern recognition field. Handwritten city name recognition is the part of postal automation. For assessing the performance of the existing techniques for handwritten city name recognition, a standardized dataset proves useful. But due to lack of publicly accessible benchmark dataset in Gurmukhi script, a systematic comparison of the existing techniques for Gurmukhi city name recognition is not feasible. In this paper, we have presented a dataset for Gurmukhi postal automation named as HWR-Gurmukhi_Postal_1.0 which contains total 40,000 samples of names of various cities which are written in Gurmukhi script. This dataset can be seen as a benchmark for comparison among existing techniques for handwritten city name recognition.