The A2iA Arabic Handwritten Text Recognition System at the Open HaRT2013 Evaluation
Document Analysis Systems(2014)
摘要
This paper describes the Arabic handwriting recognition systems proposed by A2iA to the NIST OpenHaRT2013 evaluation. These systems were based on an optical model using Long Short-Term Memory (LSTM) recurrent neural networks, trained to recognize the different forms of the Arabic characters directly from the image, without explicit feature extraction nor segmentation.Large vocabulary selection techniques and n-gram language modeling were used to provide a full paragraph recognition, without explicit word segmentation. Several recognition systems were also combined with the ROVER combination algorithm. The best system exceeded 80% of recognition rate.
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关键词
handwriting recognition,natural language processing,recurrent neural nets,text detection,a2ia arabic handwritten text recognition system,arabic handwriting recognition systems,lstm recurrent neural networks,openhart2013 evaluation,rover combination algorithm,full paragraph recognition,long short-term memory,n-gram language modeling,vocabulary selection techniques,large vocabulary handwriting recognition,openhart,rover,recurrent neural networks
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