Handwritten Urdu character recognition using one-dimensional BLSTM classifier
Handwritten Urdu character recognition using one-dimensional BLSTM classifier
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DOI：
10.1007/s00521-017-3146-x
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发表时间：
2019-04-01
影响因子：
6
通讯作者：
Razzak, Muhammad Imran
中科院分区：
文献类型：
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作者：
Bin Ahmed, Saad;Naz, Saeeda;Razzak, Muhammad Imran
在光学字符识别中，草书的识别由于其多样的表现形式而被认为是一项微妙的任务。每种草书都有不同的性质和相关的挑战。由于乌尔都语是源自阿拉伯文字的草书语言之一，这就是为什么它几乎具有相似的挑战和复杂性，但强度更大。我们可以根据他们使用的文字将乌尔都语和阿拉伯语分类。乌尔都语主要以纳斯塔克风格书写，而阿拉伯语则遵循纳斯塔克风格。本文提出了一种新的综合性乌尔都语手写离线数据库名称Urdu- nasta 'liq手写数据集（UNHD）。目前，没有标准和全面的乌尔都语手写数据集可供研究人员公开使用。所获得的数据集涵盖了500名作家在A4大小的纸上用自然手写的常用签名。联合国人居署已向公众开放，可从https://sites.google.com/site/researchonurdulanguage1/databases下载。我们使用递归神经网络进行了实验，并报告了手写乌尔都语字符识别的显著准确性。
The recognition of cursive script is regarded as a subtle task in optical character recognition due to its varied representation. Every cursive script has different nature and associated challenges. As Urdu is one of cursive language that is derived from Arabic script, that is why it nearly shares the similar challenges and complexities but with more intensity. We can categorize Urdu and Arabic language on basis of its script they use. Urdu is mostly written in Nasta'liq style, whereas Arabic follows Naskh style of writing. This paper presents new and comprehensive Urdu handwritten offline database name Urdu-Nasta'liq handwritten dataset (UNHD). Currently, there is no standard and comprehensive Urdu handwritten dataset available publicly for researchers. The acquired dataset covers commonly used ligatures that were written by 500 writers with their natural handwriting on A4 size paper. UNHD is publically available and can be download form https://sites.google.com/site/researchonurdulanguage1/databases. We performed experiments using recurrent neural networks and reported a significant accuracy for handwritten Urdu character recognition.