I was interested in symbol recognition recently and I start to read about it in the Internet. I got more information about preprocessing and segmentation stages, but all of it is just prestage for transformation from image to string. And all notes from Internet led me to using ready solution, like Tesseract, which do all works behind interface. However, I interested in detailed description of this process and I want to get all steps of this transformation.
Can anybody give me some links to exhaustive literature or articles about this theme? For example, Tesseract image_to_string() function algorithm. I will thankful for any help
The most straightforward way is the github page of Tesseract, especially the Wiki page of Tesseract.
Or if you want to recognize specific symbol, you can make your own recognizer using neural networks, follow this step-to-step tutorial.
Related
I have seen numerous examples and sample code for detecting emotions from a human face. I am in desperate need of some algorithm to change expressions. I am a new OpenCV learner. I am also confused if this image manipulation can be done using opencv ? Can functions such as warpaffine() be used for this ? If shall be grateful if someone can guide me in steps how to perform this eg. input a neutral face emotion and convert it to smile ?
Try using FaceAPI, it is free to use for non-commercial purposes and works brilliantly. It is well documented and easy to use.
I'm doing a business card scanner for my final examination about digital image processing, and I would ask to you how I have to preprocess a photo of a business card so tesseract can recognize text. I tried a lot of things, like erosion, dilation, thresholding, but I can't have a good result... Can you help me?
Thank you
Marco
If your concern is only about text recognition and not about preprocessing, consider using ScanTailor. It is an excellent pre-processing tool and it is open source.
If you want to implement the pre-processing yourself, you might want to have a look at this paper - especially the skew correction and the background estimation.The results of the algorithms described here are good. ScanTailor uses some of these.
I would recommend the open source C++ image processing library OpenCV combination with the open source free Optical Character Recognition (OCR) library tesseract.
Since your information of your problem isn't quite specific, i can answer your question in general
The main procedure in OCR is:
perform some kind of preprocessing on the image
text detection to get your ROI (Region of interest, the region containing your text)
character detection (take the text-only image and use it as input for tesseract
a few words about tesseract:
There is a lot of information to the library available online. It is a google open source library used for the google books OCR purpose. Can also handle layout analyzes in your image, but isn't perfect in this, therefore a preprocessing yourself and using tesseract only for the real character recognition part can lead to a better result. Feel free to question, if you still have questions, or if I missunderstood your question.
I´m a beginner on computer vision, but I know how to use some functions on opencv. I´m tryng to use Opencv for Document Recognition, I want a help to find the steps for it.
I´m thinking to use opencv example find_obj.cpp , but the documents, for example passport, has some variables, name, birthdate, pictures. So, I need a help to define the steps for it, and if is possible how function I have to use on the steps.
I'm not asking a whole code, but if anyone has any example link or you can just type a walkthrough, it is of great help.
There are two very different steps involved here. One is detecting your object, and the other is analyzing it.
For object detection, you're just trying to figure out whether the object is in the frame, and approximately where it's located. The OpenCv features framework is great for this. For some tutorials and comprehensive sample code, see the OpenCv features2d tutorials and especially the feature matching tutorial.
For analysis, you need to dig into optical character recognition (OCR). OpenCv does not include OCR libraries, but I recommend checking out tesseract-ocr, which is a great OCR library. If your documents have a fixed structured (consistent layout of text fields) then tesseract-ocr is all you need. For more advanced analysis checking out ocropus, which uses tesseract-ocr but adds layout analysis.
I was trying to build a basic Face Recognition system (PCA-Eigenfaces) using OpenCV 2.2 (from Willow Garage). I understand from many of the previous posts on Face Recognition that there is no standard open source library that can provide all the face recognition for you.
Instead, I would like to know if someone has used the functions(and integrated them):
icvCalcCovarMatrixEx_8u32fR
icvCalcEigenObjects_8u32fR
icvEigenProjection_8u32fR
et.al in the eigenobjects.cpp to form a face recognition system, because the functions seem to provide much of the required functionality along with cvSvd?
I am having a tough time trying to understand to do so since I am new to OpenCV.
Update: OpenCV 2.4.2 now comes with the very new cv::FaceRecognizer. Please see the very detailed documentation at:
http://docs.opencv.org/trunk/tutorial_face_main.html
I worked on a project with CV to recognize facial features. Most people don't understand the difference between Biometrics and Facial Recognition. There is a huge difference based on the fact that Biometrics is mainly based on histogram density matching while Facial Recognition implements this and vector support based on feature recognition from density. Check out the following link. This is the library you want to use if you are pursuing CV and Facial Recognition: www.betaface.com . Oleksander is awesome and based out of Germany, but he answers questions which is nice.
With OpenCV it's easy to get started with face detection. It comes with some predefined sets for feature detection, including face detection.
You might already know this one: OpenCV Wiki, FaceDetection
The important functions in this example are cvLoad and cvHaarDetectObjects. The first one loads the classifier and the second one applies it to an image.
The standard classifiers work pretty well. Of course you can train your own classifiers, if the standard ones don't fit your purpose.
As you said there are a lot of algorithms for face detection. Some of them might provide better results, but OpenCV is definitively a good start.
I have a set of image files that I can identify. Rather than an OCR, I'd like to search only for matches within the set. What's the ideal platform to quickly find matches?
OpenCV is an advanced computer vision library. It can recognize text blocks, colors, shapes, etc. so it might be of use.
Tesseract can be trained to handle languages, but I can't see a reason why you couldn't train it with shapes. Here's a really confusing training guide.
ImageMagick can also be useful. It's pretty hardcore endless parameter chaining, but you can get it to find images. It's not perfect for this application, but it's been done before. The documentation is insanely huge, but it's about as complete and illustrated as I could wish for (I'm a frequent user, as it's useful for quick image operations via CLI). Here's the image comparison documentation.
I would suggest OpenCV, but it's up to you. Good luck!