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Multi-Language Online Handwriting Recognition

IEEE transactions on pattern analysis and machine intelligence, 2017-06, Vol.39 (6), p.1180-1194 [Peer Reviewed Journal]

ISSN: 0162-8828 ;EISSN: 1939-3539 ;DOI: 10.1109/TPAMI.2016.2572693 ;PMID: 27244718 ;CODEN: ITPIDJ

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Citations Cited by
  • Title:
    Multi-Language Online Handwriting Recognition
  • Author: Keysers, Daniel ; Deselaers, Thomas ; Rowley, Henry A. ; Li-Lun Wang ; Carbune, Victor
  • Subjects: Character recognition ; Google ; Handwriting recognition ; Hidden Markov models ; Ink ; Online handwriting recognition ; Training ; Writing
  • Is Part Of: IEEE transactions on pattern analysis and machine intelligence, 2017-06, Vol.39 (6), p.1180-1194
  • Description: We describe Google's online handwriting recognition system that currently supports 22 scripts and 97 languages. The system's focus is on fast, high-accuracy text entry for mobile, touch-enabled devices. We use a combination of state-of-the-art components and combine them with novel additions in a flexible framework. This architecture allows us to easily transfer improvements between languages and scripts. This made it possible to build recognizers for languages that, to the best of our knowledge, are not handled by any other online handwriting recognition system. The approach also enabled us to use the same architecture both on very powerful machines for recognition in the cloud as well as on mobile devices with more limited computational power by changing some of the settings of the system. In this paper we give a general overview of the system architecture and the novel components, such as unified timeand position-based input interpretation, trainable segmentation, minimum-error rate training for feature combination, and a cascade of pruning strategies. We present experimental results for different setups. The system is currently publicly available in several Google products, for example in Google Translate and as an input method for Android devices.
  • Publisher: IEEE
  • Language: English
  • Identifier: ISSN: 0162-8828
    EISSN: 1939-3539
    DOI: 10.1109/TPAMI.2016.2572693
    PMID: 27244718
    CODEN: ITPIDJ
  • Source: IEEE Open Access Journals
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