I am very new to image processing and try to build a OCR for specific font using opencv and Python.
I am able to reduce the image below:
to this:
I have already written a small neural network to deduce character images into its respective English characters. Can someone help me to crop the cropped B/W image further into small images with just the characters?
The one way I could think of is by moving pixel by pixel and stop when a black pixel occurs. Is there any other way?
I think you should try cv::blobDetector
https://www.learnopencv.com/blob-detection-using-opencv-python-c/
Or just cv::findcountours with CV_EXTERNAL flag:
http://docs.opencv.org/3.1.0/df/d0d/tutorial_find_contours.html
Related
I want to extract the person in a image without any background.I want to do this for multiple images of the same kind.Please help me do so using python which can automate this process.
https://depositphotos.com/148319285/stock-video-man-run-on-green-screen.html
I,ve tried using canny edge detector could only find edges but couldnot crop it.Is there any alternative way to detect background and remove completely.
Background subtraction using opencv. Check the link for examples and details: https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_video/py_bg_subtraction/py_bg_subtraction.html
In case of such prepared images, you migth find useful simple thresholding: https://docs.opencv.org/3.4.0/d7/d4d/tutorial_py_thresholding.html in your case you would need to get grayscale image by extracting green channel from image. It would produce binary (black and white ONLY) image with white for background and black for object (or reverse depending on your choice).
I have two images, one image which contains a box and one without. There is a small vertical disparity between the two pictures since the camera was not at the same spot and was translated a bit. I want to cut out the box and replace the hole with the information from the other picture.
I want to achieve something like this (a slide from a computer vision course)
I thought about using the cv2.createBackgroundSubtractorMOG2() method, but it does not seem to work with only 2 pictures.
Simply subtracting the picture from another does not work either because of the disparity.
The course suggests using RANSAC to compute the most likely relationship between two pictures and subtract the area thaht changed a lot. But how do I actually fill in the holes?
Many thanks in advance!!
If you plant ot use only a pair of images (or only a few images), image stitching methods are better than background subtraction.
The steps are:
Calculate homography between the two images.
Warp the second image to overlap the second.
Replace the region with the human with pixels from the warped image.
This link shows a basic example of image stitching. You will need extra work if both images have humans in different places, but otherwise it should not be hard to tweak this code.
You can try this library for background subtraction issues. https://github.com/andrewssobral/bgslibrary
there is python wrappers of this tool.
Here is the image after the Pre Processed of a water meter reading...
But whenever I am using tesseract to recognize the digits its not giving an appropriate output.
So, I want to extract/segment out the digits part only as an region of Interest and to save it in a new image file, such that the tesseract can recognize it properly...
I am able to remove those extra noises in an image, that's why I am using this option.
Is there any way to do that ?
The Unprocessed Image is
Before you try extracting your digits from this image, try to reduce your image size so that your digit size would be about 16 pixels height. Secondly, reduce your tesseract scanned characters whitelist to "0123456789", to avoid other characters like ",.;'/" and so on being scanned (that is quite common on this type of pictures). Lowering your image size should help tesseract to dump this noise and not scan in or mix it with digits. This method should not work by 100% on this kind of image for sure, but to clear this kind of noise would be a challenge withoud a doubt by other ways. Maybe you could try to provide us with unprocessed image if you have one, lets see what is possible then.
I entered a captcha-ed website I would like to get rid of. Here is some sample images
Since the background is static and the word is so computer-generated non distorted character, I believe it is very do-able. Since passing the image directly to Tesseract (OCR engine) doesn't come a positive result. I would like to remove the captcha background before OCR.
I tried multiple background removal methods using Python-PIL
Remove all non-black pixels, which remove the lines but it wouldn't remove the small solid black box.
Apply filter mentioned another StackOverflow post, which would not remove the small solid black box. Also it is less effective than method 1.
Method 1 and 2 would give me a image like this
It seems close but Tesseract couldn't recognize the character, even after the top and bottom dot row is removed.
Create a background mask, and apply the background mask to the image.
Here is the mask image
And this is the image with the mask applied and grey lines removed
However blindly applying this mask would generate some "white holes" in the captcha character. And still Tesseract failed to find out the words.
Are there any better methods removing the static background?
Lastly how could I split the filtered image into 6 image with single character? Thanks very much.
I can give you a few ideas to have a try.
After you have applied step 3, you may thicken the black edges in the images using PIL so as the fill the white holes. And I guess you are using python-tesseract. If so, please refer to Example 4 in https://code.google.com/p/python-tesseract/wiki/CodeSnippets
In order to extract the characters, you may refer to Numpy PIL Python : crop image on whitespace or crop text with histogram Thresholds. There are methods about analysing the histogram of the image so as to locate the position of the whitespaces from which you can infer the boundary.
I would like to check whether a given image is matched with a cropped face image. I tried to crop the face from the image using OpenCV Python. Now i want to check if the input image is a match with the cropped face or not. What methods can I use with OpenCV to achieve this?
For sufficiently small (and not scientifically accurate) purposes, you could use OpenCV's template matching.
Feature extraction and matching may give you more accurate results in many cases. Face detector comes in as a part of OpenCV. Face recognition, however, is a much larger problem altogether.