In handwriting recognition, the design of relevant features is very important, but it is a daunting task. Deep neural networks are able to extract pertinent features automatically from the input image. This drops the dependency on handcrafted features, which is typically a trial and error process. In this paper, we perform an exhaustive experimental evaluation of learned against handcrafted features for Arabic handwriting recognition task. Moreover, we focus on the optimization of the competing full-word based language models by incorporating different characters and sub-words models. We extensively investigate the use of different sub-word-based language models, mainly characters, pseudo-words, morphemes and hybrid units in order to enhance the full-word handwriting recognition system for Arabic script. The proposed method allows the recognition of any out of vocabulary word as an arbitrary sequence of sub-word units. The KHATT database has been used as a benchmark for the Arabic handwriting recognition. We show that combining multiple language models enhances considerably the recognition performance for a morphologically rich language like Arabic. We achieve the state-of-the-art performance on the KHATT dataset.

中文翻译：

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通过结合不同的语言模型改进用于离线阿拉伯语手写识别的递归神经网络

在手写识别中，相关特征的设计非常重要，但却是一项艰巨的任务。深度神经网络能够从输入图像中自动提取相关特征。这降低了对手工制作功能的依赖，这通常是一个反复试验的过程。在本文中，我们针对阿拉伯手写识别任务中的手工特征进行了详尽的实验评估。此外，我们专注于通过合并不同的字符和子词模型来优化基于竞争的全词语言模型。我们广泛研究了不同的基于子词的语言模型的使用，主要是字符、伪词、词素和混合单元，以增强阿拉伯文字的全词手写识别系统。所提出的方法允许将任何词汇表外的词识别为任意的子词单元序列。KHATT 数据库已被用作阿拉伯语手写识别的基准。我们表明，结合多种语言模型可以显着提高对阿拉伯语等形态丰富的语言的识别性能。我们在 KHATT 数据集上实现了最先进的性能。