A Benchmark Gurmukhi Handwritten Character Dataset: Acquisition, Compilation, and Recognition

Gurmukhi script is used to write the official `Punjabi' language of the people of the western part of Indian Punjab. The script is having approximately 160 million native speakers. Recognition of handwritten characters in the Gurmukhi script is still in its embryonic stage due to intricate character shapes and the scarcity of standard datasets. This paper introduces a new large-scale benchmark dataset "Gurmukhi HWdb1.0" which is an important development in the handwritten character recognition of this script. This dataset has a total of 137,700 handwritten samples of 41 basic Gurmukhi characters and 10 numeral classes. Out of these, 110,160 images are used for training,13,770 images are set aside for validation, and 13,770 images are used for testing. Here, 265 individuals have contributed to the development of the dataset. Recognition of the script is carried out using a CNN architecture based on transfer learning on the VGG16 network. We fine-tuned the model and added our own fully connected layers needed for Gurmukhi characters. The proposed model is executed on this collected "Gurmukhi HWdb1.0" dataset for evaluation. A detailed comparison with different batch sizes is performed to understand the functionality of the model. Experimental results show that the proposed model can be benchmarked against the concerned dataset with a test accuracy of 98.42% for Gurmukhi characters and 97.51% for Gurmukhi numerals.