基于机器学习的视障人士文本转语音设备

U. Gawande, Nutan Rathod, Pooja Bodkhe, Pradnya Kolhe, Hema Amlani, Chetana B. Thaokar
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引用次数: 0

摘要

文档的图像包含文本和非文本字符,这些字符通常使用TTS(文本到语音)系统转换为语音或音频格式。盲人应该从这项TTS创新中受益。根据世界卫生组织的数据,印度有1.99%的盲人。因此,帮助盲人是必要的。本文提出了一种基于机器学习的文本-语音转换器。首先,在树莓派上嵌入本文提出的文本到语音转换器算法。其次,使用相机捕捉图像作为输入,TTS单元将接收这些图像。第三,树莓派配备了一个TTS单元,该单元提出了捕获的图像,TTS设备的输出使用音频放大器进行放大。第四步,将拟好的信号发送给说话人。阅读器使用户能够听到他们输入的文本。它需要从图像中提取文本并进行文本到语音的转换。使用相机模块和树莓派,使用OCR(光学字符识别)方法将文本转换为语音。该设置包括一个树莓派网络摄像头接口。树莓派的音频输出可以通过扬声器或耳机听到。转换过程中会经过几秒钟。这个设备可以让视力有障碍的人更容易从图像中阅读文本。实验结果表明,与目前最先进的文本到语音转换算法相比,该算法有了显著的改进。论文最后提出了今后的研究方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Novel Machine Learning based Text-To-Speech Device for Visually Impaired People
The images of documents contain the text and non-textual characters which are usually converted into voice or audio format using TTS (Text to Speech) systems. The blind individuals are supposed to benefit from this TTS innovation. According to the World Health Organization, India has 1.99% blind individuals. Hence, aiding the blind is necessary. In the proposed research, a machine learning based text-to-speech converter is proposed. First, a Raspberry Pi is embedded with proposed text to speech converter algorithm. Second, a camera is used to capture the images as input, which the TTS unit will receive. Third, a Raspberry Pi is equipped with a TTS unit, that proposed a captured image and the output of the TTS device is amplified using an audio amplifier. Fourth, the proposed signal is sent to the speaker. A reader enables the user to hear the text they have entered. It entails text extraction from the image and text-to-speech conversion. With a camera module and a Raspberry Pi, the OCR (optical character recognition) method is used to convert text to speech. The setup consists of a Raspberry Pi webcam interface. The audio output on the Raspberry Pi can be heard through speakers or headphones. A few seconds pass during the conversion. This device can make it easier for people who are visually challenged to read text from images. Experimental results show the significant improvement compare to the state-of-the-art text to speech conversion algorithm. Paper ended with future research direction.
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