Diagrams are widely used in society to transmit information such as circuit designs, music, mathematical formulae, architectural plans, and molecular structure. Computers must process diagrams both as images (marks on paper) and as information. A diagram recognizer translates from image to information and a diagram generator translates from information to image. Current technology for diagram generation is ahead of the technology for diagram recognition. Diagram generators have extensive knowledge of notational conventions which relate to readability and aesthetics, whereas current diagram recognizers focus on the hard constraints of the notation. To create a recognizer capable of exploiting layout information, it is expedient to reuse the expertise in existing diagram generators. In particular, we discuss the use of Lime (our editor and generator for music notation) to proofread and correct the raw output of MIDIScan (a third-party commercial recognizer for music notation). Over the past several years, this combination of software has been distributed to thousands of users.

Abstract Lime is a music editor, oriented to the production of printed music. A user manipulates the information content of the music notation, while viewing the physical appearance of the notation. To implement this, Lime must perform a complex translation from an abstract musical source representation to a graphical product. Techniques are discussed for providing automation without loss of customizability, for implementing WYSIWYG editing, for supporting multiple products (e.g. orchestral score and parts), and for providing good response time despite a slow translator. Similar design issues are encountered by editors for other types of diagrams, such as schematics, chemical formulas, and maps.

A system that recognizes common rhythmic patterns through template matching is described. The use of template matching gives the user the unusual ability to modify the set of templates used for analysis. This modification effects a tradeoff between the temporal accuracy required of the input and the complexity of the recognizable rhythm patterns that happen to be common in a particular piece of music. The evolving implementation of this algorithm has received heavy use over a six-year period and has proven itself as a practical and reliable input method for fast music transcription. It is concluded that templates demonstrably provide the necessary temporal context for accurate rhythm recognition.>