Automatic chord recognition is conventionally tackled as a general music audition task, where the desired output is a time-aligned sequence of discrete chord symbols, e.g. CMaj7, Esus2, etc. In practice, however, this presents two related challenges: one, the act of decoding a given chord sequence requires that the musician knows both the notes in the chord and how to play them on some instrument; and two, chord labeling systems do not degrade gracefully for users without significant musical training. Alternatively, we address both challenges by modeling the physical constraints of a guitar to produce human-readable representations of music audio, i.e guitar tablature via a deep convolutional network. Through training and evaluation as a standard chord recognition system, the model is able to yield representations that require minimal prior knowledge to interpret, while maintaining respectable performance compared to the state of the art.