Text line segmentation of historical documents: a survey
International Journal on Document Analysis and Recognition
DEBORA: Digital AccEss to BOoks of the RenAissance
International Journal on Document Analysis and Recognition
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Balancing error and supervision effort in interactive-predictive handwriting recognition
Proceedings of the 15th international conference on Intelligent user interfaces
Active learning strategies for handwritten text transcription
International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction
Language identification for interactive handwriting transcription of multilingual documents
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Effective balancing error and user effort in interactive handwriting recognition
Pattern Recognition Letters
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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 recognized 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.