Multi-Digit Number Recognition System: Single-Digit CNNs for Multi-Digit Detection and Recognition Using MNIST Dataset

This research focuses on creating a deep-learning model for identifying multi-digit numbers, which addresses the critical demand for accuracy in real-world applications. The study presents novel approaches to multi-digit recognition, providing a thorough resolution to an unsolved problem in the field of computer vision. In order to improve model generalization, the study makes use of convolutional neural networks (CNNs) that were trained on the MNIST dataset and augmented with rotation and scaling approaches. Multi-digit number prediction is a multi-step process. detection to isolate each digit. Each digit is then clipped and stored separately with its own label. Subsequently, the algorithm predicts the digit for each cropped picture and saves them. This method is repeated for all identified contours, with each predicted digit concatenated to get the final multi-digit prediction. Finally, the projected multi-digit sequence is compared to the ground truth for assessment. The CNN achieves remarkable training and validation accuracies of 99.60% and 99.28%, proving its ability to recognize multi-digit numbers. This study emphasizes the importance of advanced methods in developing deep learning models for multi-digit recognition, which promise enhanced automation and efficiency across a variety of digital technology industries.