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Bangladeshi Banknote Recognition in Real-time using Convolutional Neural Network for Visually Impaired People

机译:孟加拉国钞票实时认可使用卷积神经网络用于视力受损人员

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Visually impaired people face extreme difficulties in recognizing paper currencies for day-to-day transactions as the texture and shape of many currencies are very similar. In this paper, an automated system for identifying the Bangladeshi banknotes using a convolutional neural network has been proposed for helping visually impaired people. A new dataset has been created consisting of more than 70,000 images of currently available Bangladeshi banknotes. The development of the system includes dataset building, creating and training the convolutional neural network model, and testing it in real-time. For verifying the efficacy of the proposed method, it has been tested in various backgrounds. The system can identify the eight banknotes used in Bangladesh with an average accuracy of 92% and exhibit the result with both textual and auditory output. Moreover, the system is invariant of the orientation and sides of notes. Visually impaired people will be able to easily use it in daily transactions.
机译:视障人士面临极端困难,以识别日常交易的纸币,因为许多货币的质地和形状非常相似。在本文中,提出了一种用于使用卷积神经网络识别孟加拉国钞票的自动化系统,以帮助视力受损人群。创建了一个新的数据集,其中包括当前可用的孟加拉国钞票的超过70,000个图像。该系统的开发包括数据集建筑,创建和培训卷积神经网络模型,并实时测试。为了验证所提出的方法的功效,它已经在各种背景中进行了测试。该系统可以识别孟加拉国使用的八个纸币,平均精度为92%,并使用文本和听觉输出展示结果。此外,该系统是不变的笔记的方向和侧面。视障人士将能够在日常交易中轻松使用它。

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