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首页> 外文期刊>Image Processing, IET >Segmentation-based recognition system for handwritten Bangla and Devanagari words using conventional classification and transfer learning
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Segmentation-based recognition system for handwritten Bangla and Devanagari words using conventional classification and transfer learning

机译：基于分割的孟加拉和德比拉语单词使用传统分类和转移学习的分割识别系统

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摘要

Offline recognition of handwritten text in Indian regional scripts is a major area of research as nearly 910 million people use such scripts in India. Most of the reported research works on Indian script-based optical character recognition (OCR) system have focused on a single script only. Research for developing methodologies that are capable of handling more than one Indian script is yet to be focused. As such, this has motivated us to study and experiment on creating a recognition system that can handle two most popular Indian scripts, namely Bangla and Devanagari. The authors propose a system that first detects and corrects skew present in Bangla and Devanagari handwritten words, estimates the headline, and further segments the words into meaningful pseudo-characters. This is followed by extraction of three different statistical features and combination of these features with off-the-shelf classifiers to study and identify the exemplary combination. Moreover, they employ state-of-the-art convolutional neural network-based transfer learning architectures and delineate a comparison with the extracted hand-crafted features. Finally, they amalgamate the identified pseudo-characters to provide the final result. On experimentation, the proposed segmentation methodology is discerned to provide good accuracy when compared with existing methods.
机译：在印度区域脚本中的手写文本的离线识别是一个主要的研究领域，因为近910万人在印度使用这种剧本。大多数报告的基于印度脚本的光学字符识别（OCR）系统的研究工作仅集中在单个脚本上。能够处理多个印度脚本的开发方法的研究尚未聚焦。因此，这使我们有动力研究和实验创建一个可以处理两个最受欢迎的印度剧本，即孟加拉和德曼加拉的识别系统。作者提出了一个系统，首先检测和纠正孟加拉和Devanagari手写单词的倾斜，估计标题，以及进一步将单词分成有意义的伪字符。然后通过从货架上的分类器提取三种不同的统计特征和这些特征的组合来研究和识别示例性组合。此外，它们采用了最先进的基于卷积神经网络的转移学习架构，并与提取的手工制作功能进行了描述。最后，它们合并了所确定的伪字符以提供最终结果。在实验中，当与现有方法相比，探测所提出的分段方法来提供良好的准确性。

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