  Machine learning (ML) has been widely applied to image classification. Here,
we extend this application to data generated by a camera comprised of only a
standard CMOS image sensor with no lens. We first created a database of
lensless images of handwritten digits. Then, we trained a ML algorithm on this
dataset. Finally, we demonstrated that the trained ML algorithm is able to
classify the digits with accuracy as high as 99% for 2 digits. Our approach
clearly demonstrates the potential for non-human cameras in machine-based
decision-making scenarios.
