使用tesseract进行字符识别 [英] Character Recognition using tesseract

问题描述

我正在尝试与 tesseract API进行交互，我也是图像处理的新手，我在最近几天正在努力解决这个问题。我尝试过简单的算法，并且我已经达到了大约70％的准确度。

I am trying to interact with tesseract API also I am new to image processing and I am just struggling with it for last few days. I have tried simple algorithms and I have achieved around 70% accuracy.

我希望它的准确度为90 +％。图像的问题在于它们是72dpi。我也尝试提高分辨率，但没有得到好的结果我想要识别的图像被附加。

I want its accuracy to be 90+%. The problem with the images is that they are in 72dpi. I also tried to increase the resolution but did not get good results the images which I am trying to be recognized are attached.

任何帮助都会受到赞赏，如果我问了一些非常基本的东西，我很抱歉。

Any help would be appreciated and I am sorry if I asked something very basic.

编辑

我忘了提到我试图在2-2.5秒内在 Linux 平台和方法上进行所有处理和识别，以检测此答案中提到的文字需要花费大量时间。此外，我不想使用命令行解决方案，但我更喜欢 Leptonica 或 OpenCV 。

I forgot to mention that I am trying to do all the processing and recognition within 2-2.5 secs on Linux platform and method to detect the text mentioned in this answer is taking a lot of time. Also I prefer not to use command line solution but I would prefer Leptonica or OpenCV.

大部分图片都已上传这里

我已经尝试过以下方法来对票证进行二值化但没有运气

I have tried following things to binarize the tickets but no luck

• http：//www.vincent-net .com / luc / papers / 10wiley_morpho_DIAapps.pdf


• http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.193.6347&rep=rep1&type=pdf


• http：// iit。 demokritos.gr/~bgat/PatRec2006.pdf


• http://psych.stanford.edu/~jlm/pdfs /Sternberg67.pdf

• http://www.vincent-net.com/luc/papers/10wiley_morpho_DIAapps.pdf
• http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.193.6347&rep=rep1&type=pdf
• http://iit.demokritos.gr/~bgat/PatRec2006.pdf
• http://psych.stanford.edu/~jlm/pdfs/Sternberg67.pdf

票证包含

1. 有点坏光


2. 非文字区域


3. 减少分辨率

我试图直接将图像提供给tesseract API，它在平均1秒内给我70％的好成绩。但是我希望提高注意时间因素的准确性到目前为止我已经尝试了

I tried to feed the image direct to tesseract API and it is giving me 70% good results in 1 sec average. But I want to increase the accuracy in noticing the time factor So far I have tried

1. 检测图像的边缘


2. blob Blob分析


3. 使用自适应阈值对票证进行二值化

然后我尝试将这些二值化图像提供给tesseract，虽然二值化图像看起来很完美，但精度降低到50-60％以下。

Then I tried to feed those binarized images to tesseract, the accuracy reduced to less than 50-60%, though binarized image look perfect.

推荐答案

您可以尝试以下几种方法：

There are several things you could try:

• 为了能够提高准确性，您应该提高质量OCR引擎的图像，这意味着预处理图像 然后将它们送到Tesseract 。我建议为此目的调查 OpenCV 。

• To be able to improve the accuracy you should improve the quality of the image for the OCR engine, and that means preprocessing the images before feeding them to Tesseract. I suggest investigating OpenCV for this purpose.

OCR引擎的主要问题是它们不像我们那样善于识别字符。因此，即使非文字的内容有时也会被错误地识别为。因此，为了防止这种情况发生，最好检测文本区域并将其发送到Tesseract而不是发送完整图像就像你正在处理图像＃2一样。

The main problem with OCR engines is that they are not as good at recognizing characters as we are. So even things that are not text sometimes get mistakenly identified as if they were. Therefore, to prevent this from happening it's best to detect the areas of text and send those to Tesseract instead of sending the full image, like you are doing with image #2.

另一种提取图像文本区域的方法可以用这项技术以隔离它们 。

Another way to extract the text regions of an image can be done with this technique to isolate them.

当您从Tesseract获得结果时，您可以通过改善它们a / 10125538/176769>将生成的文本与字典进行比较 。

When you get the results from Tesseract, you can improve them by comparing the resulting text to a dictionary.

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