question‘s area recognition using image processing and regular expression

Overview

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Paper-Question-recognition

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question‘s area recognition using image processing and regular expression

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bilibili宣传视频: https://www.bilibili.com/video/av53893090

当前系统及库版本

Windows:win10 anaconda: Anaconda navigator 1.9.2 opencv: Opencv3 3.1.0 AipOcr: 百度ocr库最新 re: re库,自带 python:Python 3.7.0

系统总体思路

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预处理

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透视矫正:目标找到纸的四个点然后矫正成右下图的样子

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如何找到四个点? 有两大方法:Canny边缘检测和霍夫直线检测

边缘检测效果:得到近似轮廓从而获得四个点的位置

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双页试卷处理: 类似这种图片,需要把试卷分成左右两份

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我们的思路是: 1.选取中间部分的区域(如40%~60%)我们考虑上下也各截取了一部分,将该区域二值化
2.找到连续白色区域最多,但又远大于字与字空隙的距离的区域
3.计算这个区域的中心线后,这条线就是我们要找的双页试卷的切割线(橙色)

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这个方法甚至可以处理两页之间中间有直线的试卷。只要存在满足一定条件的空白区域就可以找到分割线。

文字识别 最初我们用teseeract库作识别,但是识别精度感人,jTesssBoxEditor训练字库的效果也很感人。

时间紧迫,我们就选择了百度Ocr的API接口作为文字识别的工具。缺点是,免费版低精度一天只有500次的使用次数。

自然语言处理

这一部分是划分题目的重中之重。 我们之前的想法是,判断识别出的某一段是否为题干,如果是题干,那么就把它和上面的部分划分开来。 但是,判断题干的效果并不好。原因在于: 1.ocr文字识别出现误差,没有识别出必要的题号等标志性词。 2.没有良好的分词。 3.正则表达式判断题干情感(比如:‘’请问…的答案是‘’、‘’以下…正确的是“ 这种一看就是题目的题干的结构)难以尽善尽美,往往需要后期debug才能补充; 简而言之,能否正确识别出题干,是我们最需要关心的。 只要识别出题干,那么上一题就被分出来了。

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另外还要考虑一下 注意事项,等无关紧要的东西不要被划分成题目。 划分出选择题的思路大致也和上面一样

划分题目区域

由于百度ocr接口除了文字还附带位置信息,所以我们只需要把每道题的题干+题目内容的位置信息合并就可以知道整道题的位置信息了。 ocr接口位置信息有4个变量(top,left,height,width),如下。

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一道题按理来说就包括了图中的两个红框的位置信息,只要综合考虑位置信息,一定可以得到整道题的位置信息。

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但是实际操作中出现了意外,一道题当最后一行压根没有识别出来怎么办。
解决办法是,根据下一道题的top信息来计算上一道题的height,这样就避免了 A.北京人遗址这一行没被识别出来的尴尬。

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前端

综合考虑,我们还是使用了python的tkinter库制作前端。 很简单,很朴素。

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包括了选择图片所在的文件夹,切题结果保存的文件夹,预处理,是否 人工校对,开始切题的功能。

如果开启了人工校对,切题完成后会出现校对页面。右边是原图,左边是切题结果。

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Owner
Yuta Mizuki
I'm a japanese learner and I'm a chinese student ;It's nice to meet you
Yuta Mizuki
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