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Fast multi-language LSTM-based online handwriting recognition

International journal on document analysis and recognition, 2020-06, Vol.23 (2), p.89-102 [Peer Reviewed Journal]

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Title:
Fast multi-language LSTM-based online handwriting recognition
Author: Carbune, Victor ; Gonnet, Pedro ; Deselaers, Thomas ; Rowley, Henry A. ; Daryin, Alexander ; Calvo, Marcos ; Wang, Li-Lun ; Keysers, Daniel ; Feuz, Sandro ; Gervais, Philippe
Subjects: Computer Science ; Image Processing and Computer Vision ; Original Paper ; Pattern Recognition
Is Part Of: International journal on document analysis and recognition, 2020-06, Vol.23 (2), p.89-102
Description: 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 Bézier curves. This leads to up to 10 × 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.
Publisher: Berlin/Heidelberg: Springer Berlin Heidelberg
Language: English
Identifier: ISSN: 1433-2833
EISSN: 1433-2825
DOI: 10.1007/s10032-020-00350-4
Source: Springer OA刊

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