In this paper, we present a multi-stage HMM-based text recognition system for handwritten Arabic. This system employs a novel way of representing Arabic characters by separating the core shapes from the diacritics and then representing these core shapes by smaller units which we term as sub-core shapes. This results in huge reductions in the number of models that need to be trained for the text recognition task. Further, contextual HMM modeling utilizing these sub-core shapes is presented which demonstrates that using sub-core shapes as models improves the contextual HMM system in comparison with a contextual HMM system employing the standard Arabic character shapes as models, and it leads to significantly compact recognizer at the same time. Furthermore, multi-stream contextual sub-core-shape HMMs are presented where the features computed from a sliding window form one stream and its horizontal derivative features are the second stream with each stream having different weights. The system is evaluated on two publicly available databases for different text recognition tasks including conditions where little training data are available. The presented system outperforms the standard character-shape system on all the text recognition tasks on both the databases.

中文翻译：

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使用多阶段子核心形状HMM的手写阿拉伯文本识别

在本文中，我们提出了一种基于HMM的多阶段手写阿拉伯语文本识别系统。该系统采用一种新颖的方式来表示阿拉伯字符，方法是将核心形状与变音符号分开，然后用较小的单位（称为子核心形状）来表示这些核心形状。这大大减少了需要为文本识别任务训练的模型的数量。此外，提出了利用这些子核心形状的上下文HMM建模，这表明与使用标准阿拉伯字符形状作为模型的上下文HMM系统相比，使用子核心形状作为模型可以改善上下文HMM系统。识别器在同一时间。此外，提出了多流上下文子核心形状HMM，其中从滑动窗口计算的特征形成一个流，其水平导数特征是第二个流，每个流具有不同的权重。该系统在两个公开可用的数据库上进行了评估，以用于不同的文本识别任务，包括缺少训练数据的条件。该系统在两个数据库的所有文本识别任务上均优于标准字符形状系统。