ocr

问题 I am having a hard time working with Tesseract, is there a way to improve the accuracy? How do I train it for myself, if needed? the only thing I am doing is reading the following characters, XYZ:-0123456789 that's it! The pictures always look that way. thanks! 回答1: The output of Tesseract 4.00alpha with your image is $ tesseract ICKcj.png - -l eng *: 4606 Y; 4809 Z; 698 Warning. Invalid resolution 0 dpi. Using 70 instead. Resample the picture to 50% and setting the dpi to 300: The output

问题 I am having a hard time working with Tesseract, is there a way to improve the accuracy? How do I train it for myself, if needed? the only thing I am doing is reading the following characters, XYZ:-0123456789 that's it! The pictures always look that way. thanks! 回答1: The output of Tesseract 4.00alpha with your image is $ tesseract ICKcj.png - -l eng *: 4606 Y; 4809 Z; 698 Warning. Invalid resolution 0 dpi. Using 70 instead. Resample the picture to 50% and setting the dpi to 300: The output

问题 I am having a hard time working with Tesseract, is there a way to improve the accuracy? How do I train it for myself, if needed? the only thing I am doing is reading the following characters, XYZ:-0123456789 that's it! The pictures always look that way. thanks! 回答1: The output of Tesseract 4.00alpha with your image is $ tesseract ICKcj.png - -l eng *: 4606 Y; 4809 Z; 698 Warning. Invalid resolution 0 dpi. Using 70 instead. Resample the picture to 50% and setting the dpi to 300: The output

问题 I want to use pytesseract to read digits from images. The images look as follows: The digits are dotted and in order to be able to use pytesseract, I need black connected digits on a white background . To do so, I thought about using erode and dilate as preprocessing techniques. As you can see, the images are similar, yet quite different in certain aspects. For example, the dots in the first image are darker than the background, while the dots in the second are whiter. That means, in the

问题 I want to use pytesseract to read digits from images. The images look as follows: The digits are dotted and in order to be able to use pytesseract, I need black connected digits on a white background . To do so, I thought about using erode and dilate as preprocessing techniques. As you can see, the images are similar, yet quite different in certain aspects. For example, the dots in the first image are darker than the background, while the dots in the second are whiter. That means, in the

问题 I've started a simple project in which it must get an image containing text with superscripts and then by using OCR (currently I'm using tesseract) it has to recognize the superscript characters + the normal ones. For example, we have a chemical equation such as Cl², but when I use the tesseract to recognize it, it gives me Cl2 (all in one line). So, what is the solution for this problem? Is there any other OCR API that has the ability to read superscripts? 回答1: Very good question that

问题 I am trying to refine an OCR prog I made to read the layout of a certain image that I am using. Right now, I would like my OCR prog to recognise only digits 0-9. I tried to follow the solution from the question: Limit characters tesseract is looking for But I got stuck in the part where I have to call tesseract as: tesseract input.tif output nobatch letters where does this go? 回答1: I posted some things about tesseract some time ago in SO: see Tesseract OCR Library - Learning Font. There is

问题 So, from what I can begin.. I am working with OCR. The script works pretty well for what I need. It detects the words with an accuracy which for me is ok. This is the result: 100% accuracy with attached image. from PIL import Image import pyocr.builders import os os.putenv("TESSDATA_PREFIX", "C:\\Program Files (x86)\\Tesseract-OCR") tools = pyocr.get_available_tools() tool = tools[0] langs = tool.get_available_languages() lang = langs[0] #eng file = "test.png" txt = tool.image_to_string(Image

问题 So I am trying to extract text from image. And as the quality and size of image is not good, it is giving inaccurate results. I tried few enhancements and other things with PIL but that is only worsening the quality of image. Can someone suggest some enhancement in image to get better results. Few Examples of images: 回答1: In the provided example of image the text is visually of quite good quality, so the question is how it comes that OCR gives inaccurate results? To illustrate the conclusions

问题 Using OpenCV (python) I am trying to remove the section of image which is above the border line (white area in this sample image where ORIGINAL is writtn) in the image shown below Using horizontal and vertical kernels I am able to draw the wireframe, however that does not work many times because many times due to scanning quality few horizontal or vertical lines appear outside the wireframe which causes wrong contour detection. In this image also you can see on top right there is noise which