论文标题
波斯手写的数字，性格和单词识别使用深度学习
Persian Handwritten Digit, Character and Word Recognition Using Deep Learning
论文作者
论文摘要
特定脚本的数字，字母和单词识别在当今的商业环境中具有各种应用。然而，只有有限的相关研究涉及波斯文字。在本文中，采用，修改了深层的神经网络以及通过各种densnet架构以及Xecceion来通过数据增强和测试时间扩展来进一步提高，以便为波斯语的特殊性和相应的手写提供了光学特征识别核算。 Taking advantage of dividing the databases to training, validation and test sets, as well as k-fold cross validation, the comparison of the proposed method with various state-of-the-art alternatives is performed on the basis of the HODA and Sadri databases, which offer the most comprehensive collection of samples in terms of the various handwriting styles possessed by different human beings, as well as different forms each letter may take, which depend on its position within a word.在HODA数据库中，我们的数字和角色的识别率分别为99.72％和89.99％，来自萨德里数据库的数字，字符和单词分别为99.72％，98.32％和98.82％。
Digit, letter and word recognition for a particular script has various applications in todays commercial contexts. Nevertheless, only a limited number of relevant studies have dealt with Persian scripts. In this paper, deep neural networks are utilized through various DensNet architectures, as well as the Xception, are adopted, modified and further boosted through data augmentation and test time augmentation, in order to come up with an optical character recognition accounting for the particularities of the Persian language and the corresponding handwritings. Taking advantage of dividing the databases to training, validation and test sets, as well as k-fold cross validation, the comparison of the proposed method with various state-of-the-art alternatives is performed on the basis of the HODA and Sadri databases, which offer the most comprehensive collection of samples in terms of the various handwriting styles possessed by different human beings, as well as different forms each letter may take, which depend on its position within a word. On the HODA database, we achieve recognition rates of 99.72% and 89.99% for digits and characters, being 99.72%, 98.32% and 98.82% for digits, characters and words from the Sadri database, respectively.
