I'd like to be able to detect and recognize text in an image (such as in the example shown below). I have tried many techniques such as Otsu thresholding, blob detection, filtering and general cleaning up of the image such as Gaussian Blur etc. I have then fed the pre-processed image into OCR software in order to recognize the text, however the results are unsatisfactory as the appearance of the text in my scenario can vary a lot.
What are the best techniques other than those listed can I use to detect and recognise text in images?
I'm currently using the OpenCV library.
Related
SIFT is used for feature extraction. Most of the tutorials that I have seen out there only show the features detected using SIFT. I need to identify ROI using SIFT. Images look like this but in worse condition (taken from different angles, some are blur, with more texts and numbers in other places too)
I need to extract this and then perform digit recognition:
What are the ways to segment this part? I was going for SIFT/SURF but couldn't find any tutorial to segment out the ROI. If there are any other suggestions then please provide the link.
Edit: Images that I have are grayscale
Edit1: this is just an example image I got from Google, My dataset only has grayscale images not colored
Given an scanned image containing graphs and text , how can I extract only images from that picture . Can you mention any image processing algorithms .
You could do connected component analysis, filtering out everything that does not look like character bounding boxes. An example paper is Robust Text Detection from Binarized Document Images
(https://www.researchgate.net/publication/220929376_Robust_Text_Detection_from_Binarized_Document_Images), but there are a lot of approaches. It depends on your exact needs if you get away with something simple.
There is a lot more complex stuff available, too. One example: Fast and robust text detection in images and video frames (http://ucassdl.cn/publication/ivc.pdf).
Given a logo image as a reference image, how to detect/recognize it in a cluttered natural image?
The logo may be quite small in the image, it can appear in clothes, hats, shoes, background wall etc. I have tried SIFT feature for matching without any other preprocessing, and the result is good for cases in which the size of the logo in images is big and the logo is clear. However, it fails for some cases where the scene is quite cluttered and the proportion of the logo size is quite small compared with the whole image. It seems that SIFT feature is sensitive to perspective distortions.
Anyone know some better features or ideas for logo detection/recognition in natural images? For example, training a classifier to locate candidate regions first, and then apply directly SIFT matching for further recognition. However, training a model needs many data, especially it needs manually annotating logo regions in images, and it needs re-training (needs to collect and annotate new image) if I want to apply it for new logos.
So, any suggestions for this? Detailed workflow/code/reference will be highly appreciated, thanks!
There are many algorithms from shape matching to haar classifiers. The best algorithm very depend on kind of logo.
If you want to continue with feature registration, i recommend:
For detection of small logos, use tiles. Split whole image to smaller (overlapping) tiles and perform usual detection. It will use "locality" of searched features.
Try ASIFT for affine invariant detection.
Use many template images for reference feature extraction, with different lightning , different background images (black, white, gray)
I am building an iOS app that, as a key feature, incorporates image matching. The problem is the images I need to recognize are small orienteering 10x10 plaques with simple large text on them. They can be quite reflective and will be outside(so the light conditions will be variable). Sample image
There will be up to 15 of these types of image in the pool and really all I need to detect is the text, in order to log where the user has been.
The problem I am facing is that with the image matching software I have tried, aurasma and slightly more successfully arlabs, they can't distinguish between them as they are primarily built to work with detailed images.
I need to accurately detect which plaque is being scanned and have considered using gps to refine the selection but the only reliable way I have found is to get the user to manually enter the text. One of the key attractions we have based the product around is being able to detect these images that are already in place and not have to set up any additional material.
Can anyone suggest a piece of software that would work(as is iOS friendly) or a method of detection that would be effective and interactive/pleasing for the user.
Sample environment:
http://www.orienteeringcoach.com/wp-content/uploads/2012/08/startfinishscp.jpeg
The environment can change substantially, basically anywhere a plaque could be positioned they are; fences, walls, and posts in either wooded or open areas, but overwhelmingly outdoors.
I'm not an iOs programmer, but I will try to answer from an algorithmic point of view. Essentially, you have a detection problem ("Where is the plaque?") and a classification problem ("Which one is it?"). Asking the user to keep the plaque in a pre-defined region is certainly a good idea. This solves the detection problem, which is often harder to solve with limited resources than the classification problem.
For classification, I see two alternatives:
The classic "Computer Vision" route would be feature extraction and classification. Local Binary Patterns and HOG are feature extractors known to be fast enough for mobile (the former more than the latter), and they are not too complicated to implement. Classifiers, however, are non-trivial, and you would probably have to search for an appropriate iOs library.
Alternatively, you could try to binarize the image, i.e. classify pixels as "plate" / white or "text" / black. Then you can use an error-tolerant similarity measure for comparing your binarized image with a binarized reference image of the plaque. The chamfer distance measure is a good candidate. It essentially boils down to comparing the distance transforms of your two binarized images. This is more tolerant to misalignment than comparing binary images directly. The distance transforms of the reference images can be pre-computed and stored on the device.
Personally, I would try the second approach. A (non-mobile) prototype of the second approach is relatively easy to code and evaluate with a good image processing library (OpenCV, Matlab + Image Processing Toolbox, Python, etc).
I managed to find a solution that is working quite well. Im not fully optimized yet but I think its just tweaking filters, as ill explain later on.
Initially I tried to set up opencv but it was very time consuming and a steep learning curve but it did give me an idea. The key to my problem is really detecting the characters within the image and ignoring the background, which was basically just noise. OCR was designed exactly for this purpose.
I found the free library tesseract (https://github.com/ldiqual/tesseract-ios-lib) easy to use and with plenty of customizability. At first the results were very random but applying sharpening and monochromatic filter and a color invert worked well to clean up the text. Next a marked out a target area on the ui and used that to cut out the rectangle of image to process. The speed of processing is slow on large images and this cut it dramatically. The OCR filter allowed me to restrict allowable characters and as the plaques follow a standard configuration this narrowed down the accuracy.
So far its been successful with the grey background plaques but I havent found the correct filter for the red and white editions. My goal will be to add color detection and remove the need to feed in the data type.
What's the best set of image preprocessing operations to apply to images for text recognition in EmguCV?
I've included two sample images here.
Applying a low or high pass filter won't be suitable, as the text may be of any size. I've tried median and bilateral filters, but they don't seem to affect the image much.
The ideal result would be a binary image with all the text white, and most of the rest black. This image would then be sent to the OCR engine.
Thanks
There's nothing like the best set. Keep in mind that digital images can be acquired by different capture devices and each device can embed its own preprocessing system (filters) and other characteristics that can drastically change the image and even add noises to them. So every case would have to be treated (preprocessed) differently.
However, there are commmon operations that can be used to improve the detection, for instance, a very basic one would be to convert the image to grayscale and apply a threshold to binarize the image. Another technique I've used before is the bounding box, which allows you to detect the text region. To remove noises from images you might be interested in erode/dilate operations. I demonstrate some of these operations on this post.
Also, there are other interesting posts about OCR and OpenCV that you should take a look:
Simple Digit Recognition OCR in OpenCV-Python
Basic OCR in OpenCV
Now, just to show you a simple approach that can be used with your sample image, this is the result of inverting the color and applying a threshold:
cv::Mat new_img = cv::imread(argv[1]);
cv::bitwise_not(new_img, new_img);
double thres = 100;
double color = 255;
cv::threshold(new_img, new_img, thres, color, CV_THRESH_BINARY);
cv::imwrite("inv_thres.png", new_img);
Try morphological image processing. Have a look at this. However, it works only on binary images - so you will have to binarize the image( threshold?). Although, it is simple, it is dependent on font size, so one structure element will not work for all font sizes. If you want a generic solution, there are a number of papers for text detection in images - A search of this term in google scholar should provide you with some useful publications.