论文标题
使用Quad树分解的基于纹理脚本标识的新方法
A New Approach for Texture based Script Identification At Block Level using Quad Tree Decomposition
论文作者
论文摘要
在为指示脚本开发单语OCR系统方面，已经取得了相当大的成功。但是，在像印度这样的国家，多脚本场景普遍存在，事先识别脚本变得强制性。在本文中，我们介绍了Gabor小波过滤器在提取11个官方手写脚本的定向能量和熵分布中的重要性，即孟加拉国，孟加拉国，Devanagari，Gujarati，Gujarati，Gurumukhi，Gurumukhi，Kannada，Kannada，Malayalam，Malayalam，Oriya，Oriya，Temil，Telugu，Urdu和Roman和Roman。实验是基于四季树的分解方法在块级上进行的，并使用六个不同知名的分类器进行了评估。最后，通过多层感知器（MLP）分类器实现了96.86％的最佳识别精度，以在2级分解时进行3倍的交叉验证。结果有助于确定当前方法对手写指示脚本分类的功效
A considerable amount of success has been achieved in developing monolingual OCR systems for Indic scripts. But in a country like India, where multi-script scenario is prevalent, identifying scripts beforehand becomes obligatory. In this paper, we present the significance of Gabor wavelets filters in extracting directional energy and entropy distributions for 11 official handwritten scripts namely, Bangla, Devanagari, Gujarati, Gurumukhi, Kannada, Malayalam, Oriya, Tamil, Telugu, Urdu and Roman. The experimentation is conducted at block level based on a quad-tree decomposition approach and evaluated using six different well-known classifiers. Finally, the best identification accuracy of 96.86% has been achieved by Multi Layer Perceptron (MLP) classifier for 3-fold cross validation at level-2 decomposition. The results serve to establish the efficacy of the present approach to the classification of handwritten Indic scripts
