Procrash: A Solution To Procrastination by Limiting Online Distractions using Optical Character Recognition

Amanda Zhu, Baoyu Yin, Yu Sun
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Abstract

For this project, I decided to relieve the tension of procrastination that commonly happens in students and adults. To find a solution to this, I created a program that uses Google Cloud Vision API (Optical Character Recognition) to detect the distracting forms of media such as Twitter, YouTube, and Facebook, and counts the number of times the user visits these websites. After a certain number of visits, the program sends a notification to remind the user to stay focused. If the user ignores the notification message while staying on the unapproved website, the program forces the tab to close. This application was applied to a small user study where a qualitative evaluation of the approach was conducted. After collecting data for two weeks, it concluded that the program was able to effectively reduce and limit the uses of online distractions, allowing the user to manage their time more efficiently by staying off websites they should not visit.
Procrash:通过使用光学字符识别限制在线干扰来解决拖延症
在这个项目中,我决定缓解学生和成年人经常出现的拖延症带来的压力。为了解决这个问题,我创建了一个程序,使用谷歌云视觉API(光学字符识别)来检测媒体的分散形式,如Twitter, YouTube和Facebook,并计算用户访问这些网站的次数。在一定次数的访问后,该程序会发送通知,提醒用户保持专注。如果用户在停留在未经批准的网站时忽略通知消息,该程序将强制关闭该选项卡。该应用程序应用于一个小型用户研究,对该方法进行了定性评估。在收集了两周的数据后,它得出结论,该程序能够有效地减少和限制在线干扰的使用,允许用户通过远离他们不应该访问的网站来更有效地管理他们的时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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