用卷积网络自动学习特征，提升乌尔都文连写字符识别准确率。
Exploration of Deep Learning Based Recognition for Urdu Text
- 基于CNN的端到端组件分类，避免复杂分段难题。
- 在自动生成的连写字符数据集上实现99%的组件分类准确率。
- 适合研究手写体识别或南亚文字处理的开发者参考。
乌尔都语是一种连笔书写语言，与阿拉伯语及其他南亚语言有相似性，因其复杂的几何和形态结构而难以分类。若分段技术不高效，基于分段的识别方法往往误差较高。本文提出一种基于组件的乌尔都文光学字符识别方法，采用卷积神经网络（CNN）进行自动特征学习。训练与测试使用通过三字符排列生成的乌尔都文本数据集，并利用连通域技术剔除非连写图像，仅保留连写部分。采用两级分层神经网络处理三种字符排列层次及组件分类任务。模型在组件分类上达到99%的准确率。
原文摘要 · Abstract (English)
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.
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