A Paper on Preparation of Dataset for Handwritten Dzongkha Alphabets
Keywords:
Deep Learning, Convolutional Neural Networks, Dzongkha, Bhutanese datasetAbstract
In this paper, we present the complete methodology of preparing a dataset for handwritten Dzongkha alphabets of Bhutan to promote the development of the Handwritten Dzongkha Alphabet Recognition System (HanDARS). The dataset consists of 30 classes, each representing a character of the Dzongkha language with 500 images in each class amounting to a total of 15000 images. The images were manually collected from different individuals and were then augmented to add more varieties to the dataset. The alphabet images were converted to binary format. This dataset can be utilized as a basis for further research and development in the field of optical character recognition for the Dzongkha language. In the future, a greater number of handwritten alphabets needs to be collected to introduce variations in the dataset.
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