Institute for Information Technology and Communications (IIKT), Otto von Guericke University, 39106 Magdeburg, Germany.
Sensors (Basel). 2018 Aug 24;18(9):2786. doi: 10.3390/s18092786.
The majority of handwritten word recognition strategies are constructed on learning-based generative frameworks from letter or word training samples. Theoretically, constructing recognition models through discriminative learning should be the more effective alternative. The primary goal of this research is to compare the performances of discriminative and generative recognition strategies, which are described by generatively-trained hidden Markov modeling (HMM), discriminatively-trained conditional random fields (CRF) and discriminatively-trained hidden-state CRF (HCRF). With learning samples obtained from two dissimilar databases, we initially trained and applied an HMM classification scheme. To enable HMM classifiers to effectively reject incorrect and out-of-vocabulary segmentation, we enhance the models with adaptive threshold schemes. Aside from proposing such schemes for HMM classifiers, this research introduces CRF and HCRF classifiers in the recognition of offline Arabic handwritten words. Furthermore, the efficiencies of all three strategies are fully assessed using two dissimilar databases. Recognition outcomes for both words and letters are presented, with the pros and cons of each strategy emphasized.
大多数手写文字识别策略是基于学习的生成框架，从字母或单词训练样本中构建的。从理论上讲，通过判别式学习构建识别模型应该是更有效的选择。本研究的主要目标是比较判别式和生成式识别策略的性能，这些策略由生成式训练的隐马尔可夫模型 (HMM)、判别式训练的条件随机场 (CRF) 和判别式训练的隐状态 CRF (HCRF) 描述。使用从两个不同数据库中获得的学习样本，我们最初训练和应用了 HMM 分类方案。为了使 HMM 分类器能够有效地拒绝不正确和不在词汇表中的分词，我们使用自适应阈值方案增强了模型。除了为 HMM 分类器提出这样的方案外，本研究还在离线阿拉伯手写词的识别中引入了 CRF 和 HCRF 分类器。此外，还使用两个不同的数据库充分评估了所有三种策略的效率。展示了单词和字母的识别结果，并强调了每种策略的优缺点。
