Self-supervised adaptation for on-line text recognition

We developed a handwritten text recognizer for on-line text written on a touch-terminal. This system is based on the activation-verification cognitive model. It is composed of three experts dedicated respectively to signal segmentation in symbols, symbol classification and lexical analysis of the classification results. The baseline system is writer-independent. We present in this paper several strategies of self-supervised writer-adaptation that we compare to the supervised adaptation scheme. The best strategy called "prototype dynamic management" modifies the recognizer parameters allowing to get results close to the supervised methods. Results are presented on a 90 texts (5400 words) database written by 38 different writers.