使用深度学习模型在摄像头捕捉的普什图文件图像中检测普什图文字和图形

IF 3.5 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Khan Bahadar, Riaz Ahmad, Khursheed Aurangzeb, Siraj Muhammad, Khalil Ullah, Ibrar Hussain, Ikram Syed, Muhammad Shahid Anwar
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引用次数: 0

摘要

布局分析是典型的文档图像分析(DIA)系统的主要组成部分,在预处理中发挥着重要作用。然而,关于普什图语的文档图像,迄今为止尚未进行过研究。本研究首次对普什图语文本和图形进行了研究,并提出了一种基于深度学习的分类器,可以检测每份文档中的普什图语文本和图形。本研究的另一个显著贡献是创建了一个真实的数据集,其中包含由相机拍摄的 1,000 多张普什图语文档图像。对于这个数据集,我们采用了深度学习技术的卷积神经网络(CNN)。我们打算采用的方法是基于快速 R-CNN 高级经典变体的开发,该变体被称为 "单枪检测器"(SSD)。评估是通过检查测试集中的 300 幅图像进行的。通过这种方法,我们的平均精确度(mAP)达到了 84.90%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Pashto script and graphics detection in camera captured Pashto document images using deep learning model
Layout analysis is the main component of a typical Document Image Analysis (DIA) system and plays an important role in pre-processing. However, regarding the Pashto language, the document images have not been explored so far. This research, for the first time, examines Pashto text along with graphics and proposes a deep learning-based classifier that can detect Pashto text and graphics per document. Another notable contribution of this research is the creation of a real dataset, which contains more than 1,000 images of the Pashto documents captured by a camera. For this dataset, we applied the convolution neural network (CNN) following a deep learning technique. Our intended method is based on the development of the advanced and classical variant of Faster R-CNN called Single-Shot Detector (SSD). The evaluation was performed by examining the 300 images from the test set. Through this way, we achieved a mean average precision (mAP) of 84.90%.
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来源期刊
PeerJ Computer Science
PeerJ Computer Science Computer Science-General Computer Science
CiteScore
6.10
自引率
5.30%
发文量
332
审稿时长
10 weeks
期刊介绍: PeerJ Computer Science is the new open access journal covering all subject areas in computer science, with the backing of a prestigious advisory board and more than 300 academic editors.
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