There are many advantages in having engineering drawings in electronic computer-aided design (CAD) format rather than hard copy only: CAD drawings are easier to modify, faster to copy and retrieve, easier to extract information from, and, in general, easier to manage Nevertheless, most engineering drawings in active use today, and most of those being produced, are still in hard copy format only. Cost-benefit analyses show that, if a drawing is expected to be modified several times, it is advantageous to convert that drawing from hard copy to electronic format. Several commercial systems are available to convert paper drawings to CAD in an automated way. This paper presents a discussion of the different aspects and requirements of the CAD conversion problem, and describes the general architecture and algorithms of one commercially available CAD conversion system, the GTX 5000. Scanning, vectorization, text recognition, symbol recognition, context processing, and cleanup editing subsystems of the GTX 5000 are described. Comparisons of several possible alternative approaches and algorithms are also presented. Finally, EPRI (Electric Power Research Institute) funded enhancements are discussed, including neural networks for character and symbol recognition, touching and broken character processing, and text/symbol associativity.