Offline handwritten character recognition is a conversion process of handwriting into machine-encoded text and predominantly used for digitizing handwritten texts and forensic applications. Currently, several techniques and methods are proposed to enhance accuracy of offline handwritten character recognition for many languages spoken across the globe like English, Tamil, Chinese and Arabic. In this paper, a local feature-based approach using supervised learning techniques is proposed to enhance the accuracy of handwritten offline character recognition for Thai alphabets using unsupervised learning for individual character as a class, whereas most of the existing methodologies for Thai character recognition is done with group of similarly looking characters as a class. The classification is operated by using support vector machine (SVM). The accuracy would be the percentage of correct classification for each class. For the result, the highest accuracy is 74.32% which has 144-bit shape features and uniform pattern LBP for the features.

中文翻译：

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有指导的学习方法在离线手写泰语字符识别中的作用

离线手写字符识别是将手写体转换为机器编码的文本的过程，主要用于数字化手写体文本和取证应用程序。当前，提出了几种技术和方法以提高针对全球使用的许多语言（例如英语，泰米尔语，中文和阿拉伯语）的离线手写字符识别的准确性。在本文中，提出了一种使用监督学习技术的基于局部特征的方法，以通过对单个字符进行无监督学习来提高泰语字母的手写离线字符识别的准确性，而大多数现有的泰语字符识别方法都是与一组看起来相似的角色作为一个类。通过使用支持向量机（SVM）进行分类。准确性将是每个类别的正确分类的百分比。结果是，最高精度为74.32％，具有144位形状特征和特征的均匀图案LBP。