We describe an online handwriting system that is able to support 102 languages using a deep neural network architecture. This new system has completely replaced our previous Segment-and-Decode-based system and reduced the error rate by 20%-40% relative for most languages. Further, we report new state-of-the-art results on IAM-OnDB for both the open and closed dataset setting. The system combines methods from sequence recognition with a new input encoding using B\'ezier curves. This leads to up to 10x faster recognition times compared to our previous system. Through a series of experiments we determine the optimal configuration of our models and report the results of our setup on a number of additional public datasets.
@article{arxiv.1902.10525,
title = {Fast Multi-language LSTM-based Online Handwriting Recognition},
author = {Victor Carbune and Pedro Gonnet and Thomas Deselaers and Henry A. Rowley and Alexander Daryin and Marcos Calvo and Li-Lun Wang and Daniel Keysers and Sandro Feuz and Philippe Gervais},
journal= {arXiv preprint arXiv:1902.10525},
year = {2020}
}