i am doing OCR project using c++ and opencv. I have some black and white images of separated handwritten characters. I want to extract unique features from those images in order to classify them using LIBSVM. can any one tell me what are the suitable algorithms for feature extraction in opencv?
You can read this. And try this.
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I am trying to perform text image restoration and I can find no proper documentation on how to perform OMP or K-SVD in C++ using opencv.
I have over 1000 training images of different sizes so do I divide images into equal sized patches or resize all images? How do I construct the signal matrix X?
What other pre-processing steps are required for sparse coding? How to actually perform K-SVD on color images?
What data type is available in OpenCV for an image dictionary and how do I initialize the Dictionary D?
I have these very basic questions and have tried to use various libraries but they don't make the working very clear.
I found this code useful. This is the only implementation in opencv I have come across so far. I guess it uses a single image for dictionary learning whereas I have to use at least 1000 images. But it certainly provides a good guideline.
I am currently working with the Tesseract OCR engine and I'm using it in conjunction with OpenCV for pre-processing the image before sending it to the OCR engine. However, I was wondering if Tesseract itself is performing some image pre-processing before extracting the text. If so, what are the methods that Tesseract implements?
My objective is to ensure I don't perform redundant pre-processing methods. Some of the pre-processing methods I perform are adaptiveThreshold and GaussianBlur.
Any help/guidance would be much appreciated!
EDIT:
I understand Tesseract does basic image pre-processing. I would like to know if it is possible to bypass these methods and directly feed in an image that I processed manually. (Again, in order to avoid redundant processing on the image)?
Tesseract uses Leptonica library for doing various pre-processing operations like Otsu binarization algorithm, dilation, erosion and so on.
But because the operations are not dependent to your data, they will cause bad results in some cases.
For more information read this page.
Currently i am working on opencv. I have an image with a text. And i want to find out the style(Bold, Italic) of the text. How can i achieve this? Thanks
What you can do is (assuming a letter by letter approach)
Using segmentation techniques you first segment out the letters
Using the segmented letters,compare against your owns data set of pre-segmented/pre-filtered letters to find the font style.
Comparison can be done using various features, SIFT,SURF,BRISK,Harris corners, template matching, or your come up with something of your own. My best guess would be to go with HAAR-features and training.
Once you get a set of features for a letter, matching for closest candidate against your pre-filtered dataset can be achieved using different techniques such as KNN, euclidean distance, etc If you use HAAR features, OpenCV can help alot in retrieval.
Eventually you might ending doing some OCR which includes font style.
OpenCV has a set of built in feature descriptors which you can read here
Good Luck!
This might help you, I know it's not exact. But it will suffice for my similar project.
"Typefont is an experimental library that detects the font of a text in a image."
https://github.com/Vasile-Peste/Typefont
I have a simple template grayscale image, with white background and black shape over it, and I have several similar test images, I want to compare these two images and see if template matches any of the test images. Can you please suggest a simple(easy to use) pattern recognition library for C++ which takes two images and compares them and shows the result?
Just do image1-image2 for all pixels. Then sum up all the differences. The lower the results, the closer the images.
If your pattern could be of several sizes, then you have to resize it and check it for each positions.
Implement a Neural Network on the image. Inputs should be the greyscales of your image. you should train your network to a train set, chose proper regularization parameters using a cross validation set, and finally test your network on a test set.
http://www.codeproject.com/Articles/13582/Back-propagation-Neural-Net
(I have done this myself to train a network to recognise hand written digits - it works very well.)
How simple the library you need is depends on the specific parameters of your problem. OpenCV is a great image processing library that should be able to do what you need it to. Here is a tutorial on template matching in OpenCV. It makes it very easy to switch between matching metrics and choose the best one for your problem.
Does anyone know of a c++ library for taking an image and performing image recognition on it such that it can find letters based on a given font and/or font height? Even one that doesn't let you select a font would be nice (eg: readLetters(Image image).
I've been looking into this a lot lately. Your best is simply Tesseract. If you need layout analysis on top of the OCR than go with Ocropus (which in turn uses Tesseract to do the OCR). Layout analysis refers to being able to detect position of text on the image and do things like line segmentation, block segmentation, etc.
I've found some really good tips through experimentation with Tesseract that are worth sharing. Basically I had to do a lot of preprocessing for the image.
Upsize/Downsize your input image to 300 dpi.
Remove color from the image. Grey scale is good. I actually used a dither threshold and made my input black and white.
Cut out unnecessary junk from your image.
For all three above I used netbpm (a set of image manipulation tools for unix) to get to point where I was getting pretty much 100 percent accuracy for what I needed.
If you have a highly customized font and go with tesseract alone you have to "Train" the system -- basically you have to feed a bunch of training data. This is well documented on the tesseract-ocr site. You essentially create a new "language" for your font and pass it in with the -l parameter.
The other training mechanism I found was with Ocropus using nueral net (bpnet) training. It requires a lot of input data to build a good statistical model.
In terms of invoking Tesseract/Ocropus are both C++. It won't be as simple as ReadLines(Image) but there is an API you can check out. You can also invoke via command line.
While I cannot recommend one in particular, the term you are looking for is OCR (Optical Character Recognition).
There is tesseract-ocr which is a professional library to do this.
From there web site
The Tesseract OCR engine was one of the top 3 engines in the 1995 UNLV Accuracy test. Between 1995 and 2006 it had little work done on it, but it is probably one of the most accurate open source OCR engines available
I think what you want is Conjecture. Used to be the libgocr project. I haven't used it for a few years but it used to be very reliable if you set up a key.
The Tesseract OCR library gives pretty accurate results, its a C and C++ library.
My initial results were around 80% accurate, but applying pre-processing on the images before supplying in for OCR the results were around 95% accurate.
What is pre-preprocessing:
1) Binarize the bitmap (B&W worked better for me). How it could be done
2) Resampling your image to 300 dpi
3) Save your image in a lossless format, such as LZW TIFF or CCITT Group 4 TIFF.