Language Identification of Character Images Using Machine Learning Techniques

In this paper, we propose a new approach for identifying the language type of character images. We do this by classifying individual character images to determine the language boundaries in multilingual documents. Two effective methods are considered for this purpose: the prototype classification method and support vector machines (SVM). Due to the large size of our training dataset, we further propose a technique to speed up the training process for both methods. Applying the two methods to classifying characters into Chinese, English, and Japanese (including Hiragana and Katakana) has produced very accurate and comparable test results. Keywords language identification, prototype classification method, support vector machines (SVM)

• Type: Conference
• Year: 2005
• Where: ICDAR
• Authors: Ying-Ho Liu, Fu Chang, Chin-Chin Lin
• Keywords: Character Images, Document Analysis, ICDAR 2005, Prototype Classification Method, Support Vector Machines