  In this article we describe the development of machine learning models to
assist the CLAS12 tracking algorithm by identifying tracks through inferring
missing segments in the drift chambers. Auto encoders are used to reconstruct
missing segments from track trajectory. Implemented neural network was able to
reliably reconstruct missing segment positions with accuracy of $\approx 0.35$
wires, and lead to recovery of missing tracks with accuracy of $>99.8\%$.
