The problem DigiScan solves

In future work, an efficient model for detecting multiple digits in a single frame is being developed. This model will be used for tasks such as detected numbers on a number plate, phone number, or cheque number.
Healthcare sector: Digit recognition systems have been used in the healthcare industry to recognize handwritten digits on medical forms, prescriptions, and lab reports. This has helped to improve the accuracy and speed of data entry, leading to better patient care.
Education sector: Digit recognition systems have been used in the education sector to grade exams and recognize handwritten digits on student assignments. This has helped to reduce the workload of teachers and improve the speed and accuracy of grading.
Overall, digit recognition systems have helped to improve the efficiency, accuracy, and speed of various tasks across different industries, leading to better outcomes for businesses and individuals alike.

Challenges we ran into

Computational complexity: Digit recognition systems can require significant computational resources, especially when using deep learning models. This can make training and inference slow
also we came across various python libraries we were not aware of ,first we had to get familiar with the libraries.
there were various other problems but we worked on these problems together as a team and we overcame them.