Outputs (2)

Improved Arabic Characters Recognition by Combining Multiple Machine Learning Classifiers (2017)
Presentation / Conference Contribution
Alabbas, M., Khudeyer, R., Jaf, S., Dong, M., Tseng, Y.-H., Lu, Y., Yu, L.-C., Lee, L.-H., Wu, C.-H., & Li, H. (2016, November). Improved Arabic Characters Recognition by Combining Multiple Machine Learning Classifiers. Presented at The 20th International Conference on Asian Language Processing., Tainan, Taiwan

In this paper, we investigate a range of strategies for combining multiple machine learning techniques for recognizing Arabic characters, where we are faced with imperfect and dimensionally variable input characters. Experimental results show that co... Read More about Improved Arabic Characters Recognition by Combining Multiple Machine Learning Classifiers.

A Semi-automatic Approach to Identifying and Unifying Ambiguously Encoded Arabic-Based Characters (2017)
Presentation / Conference Contribution
Jaf, S., Dong, M., Tseng, Y.-H., Lu, Y., Yu, L.-C., Lee, L.-H., Wu, C.-H., & Li, H. (2016, November). A Semi-automatic Approach to Identifying and Unifying Ambiguously Encoded Arabic-Based Characters. Presented at The 20th International Conference on Asian Language Processing., Tainan, Taiwan

In this study, we outline a potential problem in normalising texts that are based on a modified version of the Arabic alphabet. One of the main resources available for processing resource-scarce languages is raw text collected from the Internet. Many... Read More about A Semi-automatic Approach to Identifying and Unifying Ambiguously Encoded Arabic-Based Characters.