Authors - Sanjana, Sukanya Sharma, Dipty Tripathi Abstract - Handwritten digit recognition is a key application in image processing and pattern recognition, with wide usage in areas such as postal services, banking, and mobile applications. This research paper presents a performance comparison between traditional machine learning models and deep learning models for accurate handwritten digit classification. The study focuses on developing a mobile application using Flutter integrated with TensorFlow Lite and Firebase to deliver real-time predictions. The app performs preprocessing on input images and employs model inference for efficient and accurate digit recognition. The objective is to determine the most effective model in terms of speed and accuracy for on-device predictions, emphasizing usability and real-time response
