Self-Supervised Adaptation for on-Line Script Text Recognition

Abstract

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 activationverification 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 %.

OHRS-MEWA: On-Line Handwriting Recognition System with Multi-environment Writer Adaptation

“…There have been great researches on the field of writer adaptation which can be classified in three groups: systems reorganizing the prototypes of the database (addition, modification and deletion) [10], [11], [12], systems updating the parameters of the recognition system [15], [14], [13], [16], [6] and systems adapting without modifying the classifier's parameters [7], [8], [1], [5].…”

Improved Neural Based Writer Adaptation for On-Line Recognition Systems

“…Most recognition systems adapt by modifying their parameters values. Firstly, we indicate the prototype based systems which can be adapted to a new writing style by reorganizing the standard prototype set or using also a new writer-dependent prototype set [14] [15] [16]. Secondly, we point out the systems which update their parameters values.…”