Abstract
Urdu is a cursive script language and has similarities with Arabic and many other South Asian languages. Urdu is difficult to classify due to its complex geometrical and morphological structure. Character classification can be processed further if segmentation technique is efficient, but due to context sensitivity in Urdu, segmentation-based recognition often results with high error rate. Our proposed approach for Urdu optical character recognition system is a component-based classification relying on automatic feature learning technique called convolutional neural network. CNN is trained and tested on Urdu text dataset, which is generated through permutation process of three characters and further proceeds to discarding unnecessary images by applying connected component technique in order to obtain ligature only. Hierarchical neural network is implemented with two levels to deal with three degrees of character permutations and component classification Our model successfully achieved 0.99% for component classification.
Abstract (translated)
乌尔都语是一种连笔书写语言，与阿拉伯语及其他许多南亚语言有相似之处。由于其复杂的几何结构和形态特征，乌尔都语难以分类。如果分割技术高效，则可以进一步处理字符分类，但由于乌尔都语的上下文敏感性，基于分割的方法通常会导致较高的错误率。我们提出的乌尔都语光学字符识别系统方法是一种基于组件的分类法，依赖于自动特征学习技术——卷积神经网络（CNN）。该 CNN 在通过三个字符排列生成的乌尔都语文本数据集上进行训练和测试，并进一步采用连通成分技术丢弃不必要的图像以获取连字。我们实施了具有两个级别的分层神经网络来处理三种字符排列程度及组件分类。我们的模型在组件分类方面成功达到了 0.99% 的精度。
URL
https://arxiv.org/abs/2508.13245
