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首页> 外文期刊>Malaysian Journal of Computer Science >AUTOMATIC LINE-LEVEL SCRIPT IDENTIFICATION FROM HANDWRITTEN DOCUMENT IMAGES - A REGION-WISE CLASSIFICATION FRAMEWORK FOR INDIAN SUBCONTINENT
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AUTOMATIC LINE-LEVEL SCRIPT IDENTIFICATION FROM HANDWRITTEN DOCUMENT IMAGES - A REGION-WISE CLASSIFICATION FRAMEWORK FOR INDIAN SUBCONTINENT

机译：手写文档图像的自动行级脚本识别-印度次大陆的区域明智分类框架

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摘要

Script identification is a well-studied problem for automatic processing of document images. Several attempts have been made so far, but it is still far ahead from the complete solution. In this paper, an automatic approach for line-level handwritten script identification (HSI), considering eight official Indic scripts namely: Bangla, Devanagari, Kannada, Malayalam, Oriya, Roman, Telugu, and Urdu is proposed. We consider a 148-dimensional feature vector using: image component fractal dimension, structural and visual appearance, directional stroke, interpolation and Gabor energy based texture features. For classification, we divide the whole script dataset based on different regions of India, to study a region-wise classification performance. Experimentation was carried out using the state-of-the-art classifiers: multilayer perceptron (MLP), support vector machine (SVM), random forest (RF), and fuzzy unordered rule induction algorithm (FURIA). Among all, we found that MLP as the best performer in terms of average accuracy of 98.2%, 99.5%, 99.1%, 99.5%, 99.9%, 98%, 98.9% for eight-script, bi-script, eastern, north, south Indian script groups, scripts with atra?vs without atra? and dravidian vs. non-dravidian groups respectively.
机译：对于自动处理文档图像，脚本识别是一个经过充分研究的问题。到目前为止，已经进行了几次尝试，但是与完整的解决方案相比仍然遥遥领先。本文提出了一种行级手写脚本识别（HSI）的自动方法，其中考虑了八个官方的印度语脚本：Bangla，Devanagari，Kannada，Malayalam，Oriya，Roman，Telugu和Urdu。我们考虑使用一个148维特征向量：图像分量的分形维数，结构和视觉外观，方向笔划，插值和基于Gabor能量的纹理特征。对于分类，我们根据印度不同地区划分整个脚本数据集，以研究按地区分类的性能。实验使用最新的分类器进行：多层感知器（MLP），支持向量机（SVM），随机森林（RF）和模糊无序规则归纳算法（FURIA）。在所有语言中，我们发现MLP的最佳表现为8脚本，2脚本，东部，北部，南印度文字组，带有atra的脚本？和德拉威族与非德拉维亚族。

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