Recognition experiments have been performed on handprinted characters using a set of features which has not been applied previously to handprinting. Glucksman's "characteristic loci" were utilized in experiments with the well-known Highleyman data, as well as samples generated at Stanford Research Institute and Honeywell. Two recognition algorithms were tested. Results on numeric samples compare favorably with those of other investigators despite the small dimensionality of the feature vector. On the constrained Honeywell samples, recognition rates exceeding 98 percent were achieved using the simpler algorithm. With alphabetic samples, some problems remain in resolving persistent ambiguities, and methods for attacking these problems are considered.

Closed form expressions have been obtained for the power density spectra of signal waveshapes in use for digital magnetic recording. The ``signal'' is the magnetization versus distance profile imposed on the magnetic medium to encode binary ones and zeros. Two general classes of signals are distinguished in terms of neighboring bit-to-bit correlation. Standard recording methods such as saturation NRZ and phase modulation are considered, as well as techniques requiring bias current, such as sinusoidal frequency-shift modulation. The spectra are compared with frequency response curves for the reading head at several recorded bit densities. The more ideal bandwidth characteristics of the newer phase- and frequency-modulation techniques help to explain their improved performance at high bit densities.