11. A method for generating a teaching ink data set comprising: receiving a first set of ink input from a plurality of ink sources, at least one of the ink sources being selected from the group consisting of implicit field data, scripted untruthed ink, scripted truthed ink, and ink from at least one other language of a same script as a target language for a recognizer; generating a first teaching ink data set based upon executing a first plurality of machine learning methods selected from the group consisting of a character n-gram distribution leveling method, an allograph method, a subject diversity method, a machine truthing method, and a print and cursive data selection method upon the first set of ink input; receiving a second set of ink input from a plurality of ink sources, at least one of the ink sources being selected from the group consisting of implicit field data, scripted untruthed ink, scripted truthed ink, and ink from at least one other language of a same script as a target language for a recognizer; and generating a second teaching ink data set based upon executing a second plurality of machine learning methods upon the second set of ink input in light of the first teaching ink data set, the first plurality of machine learning methods comprising machine learning methods different from machine learning methods of the second plurality of machine learning methods.
15. The method of claim 11 , comprising: using the second teaching ink data set by a handwriting trainer to produce a handwriting recognizer for a target language.