使用深度学习的视障人士图像摘要器

V. V. N. V. Phani Kumar, V. Phani Teja, A. R. Kumar, V. Harshavardhan, U. Sahith
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引用次数: 0

摘要

人类面临的主要问题之一,特别是在这个信息和人通过短信相互联系的时代,是视觉障碍或有学习障碍。对于这个问题,我们的目标是提供一种解决方案,将任何输入的图像总结为有意义的数据,即我们分析图像数据并识别其中的对象,从而生成词汇并相应地生成有意义的句子。这可以通过使用多层卷积神经网络(CNN)来生成描述图像的词汇来实现,这包括生物和非生物。为此,我们使用ResNet50。长短期记忆使用生成的关键词来构建有意义的句子,即LSTM将使用来自CNN的信息来帮助生成图像的描述,我们称之为图像摘要器。我们使用gTTS库来提供音频输出。音频将基于用户的首选语言。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Image Summarizer for the Visually Impaired Using Deep Learning
One of the leading problems for Humanity, particularly in this era where information and people are interconnected with text messages, are visual impairment, or have a learning disability. For this problem we aim to provide a solution that summarizes any input image into meaningful data i.e. we analyse the image data and identify the objects in it and thereby generating vocabulary and generate meaningful sentences accordingly. This can be achieved by the use of multilayer Convolutional Neural Network (CNN) to generate vocabulary describing the images, this includes living as well as non-living things. For this we used ResNet50. And a Long Short Term Memory to construct meaningful sentences using the generated keywords i.e. LSTM will use the information from CNN to help generate a description of the image and we call this, The Image Summarizer. We use the gTTS library to provide the audio output. The audio will be based on the user’s preferred language.
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