Assessing the Performance of DL Methods in Handwritten Digit Recognition - Radhey Shyam; Shilpi Khanna; Priyanka Verma; Sakshi Maurya (International Journal of Data Structure Studies, 2023)
Handwritten digit recognition is a computer vision task that involves the automatic identification and classification of hand-written digits. The objective is to develop models capable of accurately recognizing and distinguishing digits handwritten by humans. With the development of machine learning and deep learning techniques, this field has advanced remarkably. The convolutional neural network (CNN) is the most often used technique for this purpose. By utilizing CNN, the model can learn to identify unique features of each digit, such as shape, thickness, and curvature patterns, enabling it to effectively classify and differentiate handwritten digits with high precision. Additionally, other machine learning techniques like artificial neural networks (ANN) and recurrent neural networks (RNN) can also be utilized. Handwritten digit recognition has practical uses in postal services, vehicle number plate detection, and extracting numbers from bank cheques. In this particular study, we focus on the detection and classification of handwritten digits by employing various techniques and models. Specifically, we compare the performance of ANN, CNN, and RNN models and found the accuracies to be 97.73, 96.61 and 96.13% respectively based on the evaluation. These evaluations are performed under the most popular dataset i.e., modified national institute of standards technology (MNIST) dataset. The collection consists of about 70,000 photos in grayscale that show handwritten digits from 0 to 9. The study’s outcomes have significant implications for practical applications across different domains.
Keywords: ANN; CNN; Digit recognition; MNIST; RNN