Intelligent character recognition using fully convolutional neural networks
作者：
Highlights：
• We believe this is the first handwriting recognition paper to achieve state-of-the-art results on both dictionary based and arbitrary symbol based handwriting recognition benchmarks.
• Uses fully convolutional neural networks with an elegant series of even tap filters to center align each character found in a word for a very simplistic and effective architecture.
• A preprocessing step normalizes input blocks to a canonical representation; which negates the need for costly recurrent symbol alignment correction.
• When a dictionary is known, we further introduce a probabilistic character error rate to correct errant word blocks.
• Our fully convolutional method uses over 100 symbols such that a single model can decipher letters, numbers, and symbols from multiple Latin-based languages.
摘要
•We believe this is the first handwriting recognition paper to achieve state-of-the-art results on both dictionary based and arbitrary symbol based handwriting recognition benchmarks.•Uses fully convolutional neural networks with an elegant series of even tap filters to center align each character found in a word for a very simplistic and effective architecture.•A preprocessing step normalizes input blocks to a canonical representation; which negates the need for costly recurrent symbol alignment correction.•When a dictionary is known, we further introduce a probabilistic character error rate to correct errant word blocks.•Our fully convolutional method uses over 100 symbols such that a single model can decipher letters, numbers, and symbols from multiple Latin-based languages.
论文关键词：Handwriting recognition,Fully convolutional neural networks,Deep learning
论文评审过程：Received 2 May 2018, Revised 4 December 2018, Accepted 15 December 2018, Available online 17 December 2018, Version of Record 21 December 2018.
论文官网地址：https://doi.org/10.1016/j.patcog.2018.12.017
