BUILT A DATASET OF GUJARATI ISOLATED HANDWRITTEN CHARACTERS AND RECOGNITION THROUGH DEEP LEARNING

In the current era with the rise of new machine learning algorithms, particularly deep learning, the demand for large, high-quality datasets has grown significantly, especially in handwritten character recognition (HCR). While several Indian languages have publicly available benchmark datasets, a few, including Gujarati, still lack such resources. This paper addresses an attempt to build a dataset for Gujarati isolated handwritten characters and to recognize the isolated Gujarati handwritten vowels and consonants. The dataset is collected from 692 writers of varying ages, genders, qualifications, and professions. The dataset consists of 63,664 samples for 46 classes including 34 consonants and 12 vowels where 1384 images of each character. The proposed model was run with an 80:20 training and testing ratio, using 7, 10, 20, 30, & 40 epochs. The model showed promising results and achieved the highest training accuracy 90.92%, and the highest testing accuracy 89.51%.