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Real Time Road Sign Recognition System Using Artificial Neural Networks for Bengali Textual Information Box

机译:使用人工神经网络使用人工神经网络的实时路标孟加拉文本信息框

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An Automated Road Sign Recognition system using Artificial Neural Network for the Textual Information box inscribing in Bengali is presented in this paper. The system captures real time images every two seconds and saves them as JPG format files. The system processes the images to find out whether they contain images of road signs or not. The textual information of the road signs is detected and extracted from the images. The Bengali OCR system takes the textual information as an input to recognize individual Bengali characters. The Bengali OCR is implemented using Multi layer Perceptron. The output of the Bengali OCR system is compared with the previously enrolled standard Bengali textual road signs. The throughput which comes from the matching process is used as input for the speech synthesizer and finally the system delivers the audio stream to the driver, either in Bengali or in English based on the user settings. After testing this system, the obtained accuracy rate was evaluated at 91.48%.
机译:本文介绍了使用人工神经网络的自动化路标识别系统,孟加利铭刻孟加拉的文本信息框。系统每两秒捕获实时图像,并将其保存为JPG格式文件。系统处理图像以了解它们是否包含道路标志的图像。检测到路标的文本信息并从图像中提取。 Bengali OCR系统将文本信息作为输入以识别单个孟加拉人字符。孟加拉OCR是使用多层Perceptron实施的。将孟加拉OCR系统的输出与先前注册的标准孟加拉文本道路标志进行了比较。来自匹配过程的吞吐量用作语音合成器的输入,最后系统将音频流传递到驾驶员,或者根据用户设置以英语为单位。在测试该系统后,获得的精度率为91.48%。

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