I am trying to develop a character matching application which will take an image from a camera and match it with a provided image template. So far I have tried matchShapes of contours which is not working correctly on characters, it's working fine for simple shapes. I tried using matchTemplate but that's also not working correctly if I change size, font or rotation of character written in image captured from camera and try matching it with template image.
I am now thinking I need to do feature extraction after segmenting the camera image in sets and compare these sets with a feature set of reference images. Can anyone please give me a starting off direction or suggestion.
For example, this is an image from camera
and I need to find a template image
I must stress that I am no expert at optical character recognition so please do a thorough research on your end as well. Following are two links that might help you achieve your goal using character feature sets:
http://blog.damiles.com/2008/11/basic-ocr-in-opencv/
http://opencv-python-tutroals.readthedocs.org/en/latest/py_tutorials/py_ml/py_knn/py_knn_opencv/py_knn_opencv.html
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I am currently looking for a proper solution to the following problem, which is not directly programming oriented, but I am guessing that the users of opencv might have an idea:
My stereo camera has a sensor of 1/3.2" 752x480 resolution. I am using the two stereo images of this very camera in order to create a point cloud, thanks to the point cloud library (PCL).
The problem is that I would like to reduce the number of points contained by the point cloud, by directly lowering the resolution of the input images (passing from 752x480 to 376x240).
As it is indicated in the title, I have to adapt the focal of the camera in pixels to this need:
I calculate this very parameter thanks to the following formula:
float focal_pixel = (FOCAL_METERS / SENSOR_WIDTH_METERS)*InputImg.cols;
However the SENSOR_WIDTH_METERS is currently constant and corresponds to the 1/3.2" data converted to meters AND I would like to adapt this to the resolution that I would like to have: 376x240.
I am absolutly not sure if I turned my problem clearly enough to be answered, which would mean that I am going in the wrong direction.
Thank you in advance
edit: the function used to process the stereo image (after computing):
getPointCloud(hori_c_pp, vert_c_pp, focal_pixel, BASELINE_METERS, out_stereo_cloud, ref_texture);
where the two first parameters are just the coordinates of the center of the image, BASELINE_METERS the baseline of my camera out_stereo_cloud my output cloud and eventually ref_texture the color information. This function is taken from the sub library stereo_matching.
For some reason, if I just resize the stereo images, it seems to enter in conflict with the focal_pixel parameters, since the dimension are not the same anymore.
Im very lost on this issue.
As I don't really follow the formulas and method calls you're posting I advise you to use another approach.
OpenCV already gives you the possibility to create voxels using stereo images with the method cv::reprojectImageTo3D. Another question also already discusses the conversion to the according PCL datatype.
If you only want to reproject a certain ROI of your image you should opt for cv::perspectiveTransform as is explained in the documentation I pointed out in the first link.
I was watching this talk from pycon http://youtu.be/B1d9dpqBDVA?t=15m34s around the 15:33 mark the speaker talks about extracting lines from an image (receipt) and then feeding that to the OCR engine so that text can be extracted in a better way.
I have a similar need where I'm passing images to the OCR engine. However, I don't quite understand what he means by extracting lines from an image. What are some open source tools that I can use to extract lines from an image?
Take a look at the technique used to detect the skew angle of a text.
Groups are lines are used to isolate text on an image (this is the interesting part).
From this result you can easily detect the upper/lower limits of each line of text. The text itself will be located inside them. I've faced a similar problem before, the code might be useful to you:
All you need to do from here is crop each pair of lines and feed that as an image to Tesseract.
i can tell u a simple technique to feed the images to OCR.. just perform some operations to get the ROI (Region of Interest) of ur image, and localize the area where the image after binarizing it.. then you may find contours, and by keeping the threasholding value, and setting the required contour area, you can feed the resulting image to OCR :) ..
(sorry for bad way of explaination)
Direct answer: you extract lines from an image with Hough Transform.
You can find an analytical guide here.
Text lines can be detected as well. Karlphillip's answer is based on Hough Transform too.
I have opencv installed and working on my iphone (big thanks to this community). I'm doing template matching with it. It does find the object in the captured image. However, the exact location seems to be hard to tell.
Please take a look at the following video (18 seconds):
http://www.youtube.com/watch?v=PQnXNZMqpsU
As you can see in the video, it does find the template in the image. But when i move the camera a bit further away, then the found template is positioned somewhere inside that square. That way it's hard to tell the exact location of the found object.
The square that you see is basically the found x,y location of the template plus the width,height of the actual template image.
So basically my question is, is there a way to find the exact location of the found template image? Because currently it can be at any locastion inside that square. No real way to tell the exact location...?
It seems that you're not well-pleased with your template matching algorithm :)
Shortly, there are some ways to improve it, but I would recommend you to try something else. If your images are always as simple as in the video, you can use thresholding, contour finding, blob detection, etc. They are simple and fast.
For a more demanding environment, you may try feature matching. Look for SIFT, SURF, ORB, or other ways to describe your objects with features. Actually, ORB was specifically designed to be fast enough for the limited power of mobile phones.
Try this sample in the OCV samples/cpp/ folder
matching_to_many_images.cpp
And check this detailed answer on how to use feature detectors;
Detecting if an object from one image is in another image with OpenCV
Template matching (cvMatchTemplate()) is not invariant to scale and rotation. When you move the phone back, the image appears smaller, and the template "match" is just the place with the best match score, though it is not really a true match.
If you want scale and/or rotation invariance you will have to try non-template matching methods such as those using 2D-feature descriptors.
Check out the OpenCV samples for examples of how to do this.
I want to ask you to help to choose or find a good algorithm for the following problem:
I want to recognize the template in the image, the template is a text of non standard font, so OCR possibly will not handle it. What I want is to recognize it using the template matching algorithm. Please refer to the image:
As you see there is a background, in this image I draw it myself, and the background is simple. Usually it is not so simple: it has illuminance variations and usually colored to one color. So, I want to match this template, but I want the algorithm to be invariant of background color.
I've tryed the opencv's cvMatchTemplate, it handles well if there is a template on the image. But if I rotate object under the camera or remove it so that there will not be any templates, the algorithm finds many false-positive matches.
So I want to find an algorithm that also is rotation-invariant.
Can you suggest any?
Look at Hu Moments - is rotation & size invariant. , OpenCV has a Match shapes method which does most of the work for you.
I want to make a program for checking the printed paper for errors.
PDF File: please refer to the second page, top right picture
As you see, that system could identify the errors made by printer.
I want to know how was it achieved. What are existing documents about this?
Or any ideas you have?
Thank you
This can be very easy or very difficult.
if your images are black white and your scan is quite precise you can try with a simple subtraction between the images (scanned and pattern)
if your scan will read the image with a possible deformation or translation the you will need first an image registration algorithm.
if your scan present background noise you will have some trouble with the subtraction and then it turns very difficult.
may be some image samples can help to suggest you a more specific algorithm.
I think you need to some how compare two images in a way that is robust to deformation. As mentioned before, substracting the two images can be a first step. Another more sophisticated way can be to use distance transform (or chamfering based methods for template matching) to compare how similar the two images are in the presence of some deformation. More sophisticated solutions can use methods like shape contexts.