论文标题

嵌入式大规模手写的汉字识别

Embedded Large-Scale Handwritten Chinese Character Recognition

论文作者

Chherawala, Youssouf, Dolfing, Hans J. G. A., Dixon, Ryan S., Bellegarda, Jerome R.

论文摘要

随着手写输入变得越来越普遍,支持中国笔迹识别所需的大符号清单会带来独特的挑战。本文描述了Apple深度学习识别系统如何在各种移动设备实时运行时如何准确处理30,000个汉字。为了达到可接受的准确性,我们特别关注数据收集条件,写作风格的代表性和培训方案。我们发现,通过适当的照顾,甚至更大的库存都可以触及。我们的实验表明,只要我们使用足够质量和足够数量的培训数据,准确性只会随着库存增加而缓慢降解。

As handwriting input becomes more prevalent, the large symbol inventory required to support Chinese handwriting recognition poses unique challenges. This paper describes how the Apple deep learning recognition system can accurately handle up to 30,000 Chinese characters while running in real-time across a range of mobile devices. To achieve acceptable accuracy, we paid particular attention to data collection conditions, representativeness of writing styles, and training regimen. We found that, with proper care, even larger inventories are within reach. Our experiments show that accuracy only degrades slowly as the inventory increases, as long as we use training data of sufficient quality and in sufficient quantity.

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