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Detecting Sex From Handwritten Examples

机译:从手写示例中检测性别

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摘要

There are several tasks that human excel at and computers do not and vice-versa. Just until a few years ago computers were as good as a storage for images and videos. However, in the past 6 years with the boon in artificial neural network, labeled data and computation power; machines have started becoming smart at tasks like recognizing images, detecting different objects in images, captioning images, understanding and summarizing videos, detecting semantic actions in videos and so on. Deep learning researchers and practitioners have started demonstrating notable performance of AI(Artificial Intelligence) on many different tasks that pushes the boundaries and as a continuation of that process, we took one specific problem to solve using deep learning that even human can not solve. We have taken Bangla handwritten characters, then trained them applying several deep learning techniques such as Convolutional Neural Network and Recurrent Neural Network to predict the sex of the writer. Consequently, we have got 91.85% accuracy rate and also demonstrated further analysis of the results that we got.
机译:有几个任务,人类Excel和计算机不反之亦然。只需在几年前,计算机与图像和视频的存储一样好。但是,在过去的6年里与人工神经网络中的福音,标记数据和计算能力;机器已经开始在识别图像,检测图像中的不同对象,标题图像,了解和概述视频中的不同对象,检测视频中的语义动作等任务。深入学习研究人员和从业者开始展示了AI(人工智能)在推动界限的许多不同任务中表现出显着表现,并作为该过程的延续,我们采取了一个特定的问题来解决使用深度学习,即使是人类无法解决。我们采取了孟加拉手写的人物,然后训练他们应用了几种深度学习技术,如卷积神经网络和经常性神经网络,以预测作者的性别。因此,我们获得了91.85%的准确率,并进一步分析了我们得到的结果。

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