Open AccessSelf-supervised adaptation for on-line script text recognition
Author(s) -
Loïc Oudot,
Lionel Prévost,
Maurice Milgram
Publication year - 2005
Publication title -
elcvia. electronic letters on computer vision and image analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.15
H-Index - 11
ISSN - 1577-5097
DOI - 10.5565/rev/elcvia.98
Subject(s) - computer science , artificial intelligence , scheme (mathematics) , adaptation (eye) , word error rate , line (geometry) , machine learning , supervised learning , speech recognition , natural language processing , pattern recognition (psychology) , artificial neural network , mathematics , mathematical analysis , physics , geometry , optics
We have recently developed in our lab a text recognizer for on-line texts written on a touch-terminal. We present in this paper several strategies to adapt this recognizer in a self-supervised way to a given writer and compare them to the supervised adaptation scheme. The baseline system is based on the activation-verification cognitive model. We have designed this recognizer to be writer-independent but it may be adapted to be writer-dependent in order to increase the recognition speed and rate. The classification expert can be iteratively modified in order to learn the particularities of a writer. The best self-supervised adaptation strategy is called prototype dynamic management and gets good results, close to those of the supervised methods. The combination of supervised and self-supervised strategies increases accuracy again. Results, presented on a large database of 90 texts (5,400 words) written by 38 different writers are very encouraging with an error rate lower than 10%