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A Hybrid Model for Recognition of Online Handwriting in Indian Scripts

机译:用于识别印度文字的在线手写体的混合模型

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We present a complete online handwritten character recognition system for Indian languages that handles the ambiguities in segmentation as well as recognition of the strokes. The recognition is based on a generative model of handwriting formation, coupled with a discriminative model for classification of strokes. Such an approach can seamlessly integrate language and script information in the generative model and deal with similar strokes using the discriminative stroke classification model. The recognition is performed in a purely bottom-up fashion, starting with the strokes, and the ambiguities at each stage are reserved and transferred to the next stage for obtaining the most probable results at each stage. We also present the results of various pre-processing, feature selection and classification studies on a large data set collected from native language writers in two different Indian languages: Malayalam and Telugu. The system achieves a stroke level accuracy of 95.78% and 95.12% on Malayalam and Telugu data, respectively. The akshara level accuracy of the system is around 78% on a corpus of 60, 492 words from 367 writers.
机译:我们为印度语言提供了一个完整的在线手写字符识别系统,该系统可以处理分割和笔画识别中的歧义。该识别基于笔迹形成的生成模型以及用于笔划分类的判别模型。这种方法可以将语言和脚本信息无缝地集成到生成模型中,并使用可区分的笔划分类模型来处理相似的笔划。从笔画开始,以纯自下而上的方式执行识别,并且保留每个阶段的歧义并将其转移到下一个阶段,以便在每个阶段获得最可能的结果。我们还针对从使用两种不同印度语言(马拉雅拉姆语和泰卢固语)的母语作家收集的大数据集,提供了各种预处理,特征选择和分类研究的结果。根据马拉雅拉姆语和泰卢固语数据,该系统的笔画水平精度分别为95.78%和95.12%。在来自367位作者的60 492个单词的语料库上,该系统的akshara级别准确性约为78%。

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