Text line segmentation of historical documents: a survey
International Journal on Document Analysis and Recognition
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Adaptation from partially supervised handwritten text transcriptions
Proceedings of the 2009 international conference on Multimodal interfaces
Interactive layout analysis and transcription systems for historic handwritten documents
Proceedings of the 10th ACM symposium on Document engineering
Rejection threshold estimation for an unknown language model in an OCR task
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Active learning strategies for handwritten text transcription
International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction
Confidence measures for error discrimination in an interactive predictive parsing framework
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Language identification for interactive handwriting transcription of multilingual documents
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
A prototype for interactive speech transcription balancing error and supervision effort
Proceedings of the 2012 ACM international conference on Intelligent User Interfaces
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 modeling, n-gram language modeling, 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. However, a certain degree of supervision is required for proper model adaptation from partially supervised transcriptions. Here, we propose a simple yet effective method to find an optimal balance between recognition error and supervision effort.