Li-Lun Wang
Li-Lun Wang received his BS degree in Computer Science and Information Engineer from National Taiwan University in 2002, and PhD in Computer Science from University of Illinois at Urbana-Champaign in 2012. He joined Google Research in 2012, working on handwriting recognition. His research interests include artificial intelligence and machine learning.
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Fast Multi-language LSTM-based Online Handwriting Recognition
Thomas Deselaers
Alexander Daryin
Marcos Calvo
Sandro Feuz
Philippe Gervais
International Journal on Document Analysis and Recognition (IJDAR) (2020)
Preview abstract
Handwriting is a natural input method for many people and we continuously invest in improving the recognition quality. Here we describe and motivate the modelling and design choices that lead to a significant improvement across the 100 supported languages, based on recurrent neural networks and a variety of language models.
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This new architecture has completely replaced our previous segment-and-decode system~\cite{Google:HWRPAMI} and reduced the error rate by 30\%-40\% relative for most languages. Further, we report new state-of-the-art results on \iamondb for both the open and closed dataset setting.
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By using B\'ezier curves for shortening the input length of our sequences we obtain up to 10x faster recognition times. Through a series of experiments we determine what layers are needed and how wide and deep they should be.
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We evaluate the setup on a number of additional public datasets.
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Multi-Language Online Handwriting Recognition
Thomas Deselaers
IEEE Transactions on Pattern Analysis and Machine Intelligence (2016)
Preview abstract
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 time- and 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.
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