I"m trying to process this chalkboard image I recorded with tesseract, but am having no luck. I thought about doing pre-processing to help improve our results but am unsure of what methods to call on it.
Here is the image I have:
And here are the tesseract commands I'm running:
convert 468.jpg -bordercolor black -border 20x20 468-b.jpg
tesseract 468-b.jpg - -psm 11
Please note it doesn't have to process all the math symbols correctly, but at least get the 1 + 4 = 5 and maybe the 2 x 12 = 24 x 1.
I had a previous photo that worked well with using just these commands. Here's the photo:
And here are the results it would spit out:
I+I
2+2m
It's not perfect, but it was much better than what I was getting before. How can I improve the results of my new chalkboard image? Do I need to use OpenCV, and if so, an example implementation would be very, very helpful
Thanks in advance
P.S. Here was my original question I asked on Tesseracts GitHub that lead me to better results:
https://github.com/tesseract-ocr/tesseract/issues/468
tesseract cannot recognize handwriting image well. So I think you should use deep learning for recognizing handwriting image.
Here is some tutorial you interest in.
https://www.tensorflow.org/versions/r0.12/tutorials/mnist/beginners/index.html#mnist-for-ml-beginners
Related
I am working on a project where my task is to identify machine part by its part number written on label attached to it or engraved on its surface. One such example of label and engraved part is shown in below figures.
My task is to recognise 9 or 10 alphanumerical number (03C 997 032 D in 1st image and 357 955 531 in 2nd image). This seems to be easy task however I am facing problem in distinguishing between useful information in the image and rest of the part i.e. there are many other numbers and characters in both image and I want to focus on only mentioned numbers. I tried many things but no success as of now. Does anyone know the image pre processing methods or any ML/DL model which I should apply to get desired result?
Thanks in advance!
JD
You can use OCR to the get all characters from the image and then use regular expressions to extract the desired patterns.
You can use OCR method, like Tesseract.
Maybe, you want to clean the images before running the text-recognition system, by performing some filtering to remove noise / remove extra information, such as:
Convert to gray scale (colors are not relevant, aren't them?)
Crop to region of interest
Canny Filter
A good start can be one of this tutorial:
OpenCV OCR with Tesseract (Python API)
Recognizing text/number with OpenCV (C++ API)
I'm trying to convert a tangent space normal map to a height/displacement map. For sure this will be not 100% accurate speaking in terms of "exact height" for each pixel. But the relative height from each pixel to the next is more than enough.
Available Algorithm + Info's:
http://www.cournia.com/devnull/n2h/n2h.pdf
Questions:
1. How to convert a normal-to-height map in Photoshop/Gimp? Is there a way using these tools? Beside; I don't wan't to use CrazyBump or any other Texture-Tools. This has to run via CL later on. A Photoshop solution is more or less just a pre-step to understand workflow a bit better.
If not possible with PS/Gimp; how to include the algorithm in an imagemagick process?
I've checked already Doom3:-Normal2Height; Crazybump & all other texture tools like Nvidia's PS-Plugin, xNormal, Awesomebump, SSBump, etc. I'd need this function working with Imagemagick.
Any help is very much welcome. Python preferable.
thx
There are a couple of possibilities for doing that with ImageMagick.
Firstly, you could implement your own process module. When running configure to install ImageMagick, you would then do:
./configure --with-modules=yes
Then, when you want to apply your bumpmap processing on the command-line, you would do:
convert input.png -process analyse <param1> <param2> result.png
Your processing needs to be written in C/C++ and the best description I know of is on Alan Gibson's webpages here.
Secondly, you could write your entire processing using Magick++ which is the C++ binding to ImageMagick. Best description I know of is here with sample code here.
I am trying to determine when a food packaging have error or not error. Example
the logo " McDonald's " have error misprints or not, as the wrong label, wrong color..( i can not post picture )
What should I do, please help me!!
It's not a trivial task by any stretch of the imagination. Two images of the same identical object will always be different according to lightning conditions, perspective, shooting angle, etc.
Basically you need to:
1. Process the 2 images into "digested" data - dominant color, shapes, etcw
2. Design and run your own similarity algorithm between the 2 objects
You may want to look at Feature detectors in OpenCV: Surf, SIFT, etc.
Along a result I just found your question, so I think I come too late.
If not I think your problem car easily be resolved, it exists since years and is called Sikuli .
While it's for testing purposes, I have been using it in the same way as you need : compare a reference and a production image. Based on OpenCV it does it very well.
After spending over 10 hours to compile tesseract using libc++ so it works with OpenCV, I've got issue getting any meaningful results. I'm trying to use it for digit recognition, the image data I'm passing is a small square (50x50) image with either one or no digits in it.
I've tried using both eng and equ tessdata (from google code), the results are different but both get guess 0 digits. Using eng data I get '4\n\n' or '\n\n' as a result most of the time (even when there's no digit in the image), with confidence anywhere from 1 to 99.
Using equ data I get '\n\n' with confidence 0-4.
I also tried binarizing the image and the results are more or less the same, I don't think there's a need for it though since images are filtered pretty good.
I'm assuming that there's something wrong since the images are pretty easy to recognize compared to even simplest of the example images.
Here's the code:
Initialization:
_tess = new TessBaseAPI();
_tess->Init([dataPath cStringUsingEncoding:NSUTF8StringEncoding], "eng");
_tess->SetVariable("tessedit_char_whitelist", "0123456789");
_tess->SetVariable("classify_bln_numeric_mode", "1");
Recognition:
char *text = _tess->TesseractRect(imageData, (int)bytes_per_pixel, (int)bytes_per_line, 0, 0, (int)imageSize.width, (int)imageSize.height);
I'm getting no errors. TESSDATA_PREFIX is set properly and I've tried different methods for recognition. imageData looks ok when inspected.
Here are some sample images:
http://imgur.com/a/Kg8ar
Should this work with the regular training data?
Any help is appreciated, my first time trying tessarect out and I could have missed something.
EDIT:
I've found this:
_tess->SetPageSegMode(PSM_SINGLE_CHAR);
I'm assuming it must be used in this situation, tried it but got the same results.
I think Tesseract is a bit overkill for this stuff. You would be better off with a simple neural network, trained explicitly for your images. At my company, recently we were trying to use Tesseract on iOS for an OCR task (scanning utility bills with the camera), but it was too slow and inaccurate for our purposes (scanning took more than 30 seconds on an iPhone 4 at a tremendously low FPS). At the end, I trained a neural-network specifically for our target font, and this solution not only beat Tesseract (it could scan stuff flawlessly even on an iPhone 3Gs), but also a commercial ABBYY OCR engine, which we were given a sample from the company.
This course's material would be a good start in machine learning.
I was working with PIL, OpenCV and OCR readers to read texts from Images. The biggest problem I faced is when it comes to Image processing to make texts sharp enough for easier/accurate extraction by the OCR reader.
For that, I thought of increasing the contrast/brightness and do a histogram equlization using PIL but that didn't help the cause either.
So, what would you suggest to do to make the texts appear sharper for better text extraction?
PIL has sharpen and edge enhancing filters. Is this what you want? An example image showing what you are dealing with would be helpful.
Your image has an uneven background color which may be causing problems. Try looking at this solution to create a nice leveled b&w image.
But the black collar is also going to cause problems and you should look at ways of cropping it out.
That said, I get reasonable improvements with a simple PIL SHARPEN filter:
tesseract results after SHARPEN filter:
From what I've learned looking inside people, ^ I've decided human
beings are somewhere ` between a hurricane and an ice cube} in some
respects, permanently mysterious, but in others- with enough science
and careful probingeentirely ' scrutabler It would be as foolish to
think we have reached the limits of human knowledge as it is to 3
think we could ever know everything. There is still room enough to
get better, to ask questions of i even the dead, to learn from
knowing when our i simple certainties are wrong.
And results without filter:
From what I've learned lnnkmg wade maple} Fve deculed lunnuan wlng;.
el'. .y.w.r-a' isbetween a luurrlctuvr null llva laAll.' a. I ll
respects, permanently unyst:-rwntMl ln ms. re with enough scaena)
and turutul pmlulng l~m.rely scrutable. It would he as loallsla to
thank we have reached the llmlts of human knowledge as lt ls to think
we could ever know everything. There ls still room enough to get
better, to ask quesuons of ` even the dead, to learn from knowmg when
our simple certeindes are wrong.