Adaptation from partially supervised handwritten text transcriptions.
An effective approach to transcribe handwritten text documents is to follow an interactive-predictive paradigm in which both, the system is guided by the user, and the user is assisted by the system to complete the transcription task as efficiently as possible. This approach has been recently implemented in a system prototype called GIDOC, in which standard speech technology is adapted to handwritten text (line) images: HMM-based text image modelling, $n$-gram language modelling, and also confidence measures on recognised words. Confidence measures are used to assist the user in locating possible transcription errors, and thus validate system output after only supervising those (few) words for which the system is not highly confident. Here, we study the effect of using these partially supervised transcriptions on the adaptation of image and language models to the task.