Handwritten Digit Recognition for Native Gujarati Language Using Convolutional Neural Network

Computer vision's most active area of research is handwritten digit recognition. Numerous applications essentially motivate to build of an effective recognition model using computer vision that can empower the computers to analyze the images in parallel to human vision. Most efforts have been devoted to recognizing the handwritten digits, while less attention has been paid to the recognition of handwritten digits in resource-poor languages such as Gujarati is a native language in India mainly due to the morphological variance in the writing style. We used a CNN-based model for digit recognition and a Convolution Neural Network for classification in this paper to propose a customized deep learning model for the recognition and classification of handwritten Gujarati digits. This work makes use of the 52,000 images in the Gujarati handwritten digit dataset, which was made from a scanned copy of the Gujarati handwritten digit script. The proposed method outperforms the current state-of-the-art classification accuracy of 99.17%, as demonstrated by extensive experimental results. In addition, the precision, recall, and F1 scores were calculated to assess the classification's effectiveness.