I have a project that make use of Google Vision API DOCUMENT_TEXT_DETECTION in order to extract text from document images.
Often the API has troubles in recognizing single digits, as you can see in this image:
I suppose that the problem could be related to some algorithm of noise removal, that recognizes isolated single digits as noise. Is there a way to improve Vision response in these situations? (for example managing noise threshold or others parameters)
At other times Vision confuses digits with letters:
But if I specify as parameter languageHints = 'en' or 'mt' these digits are ignored by the ocr. Is there a way to force the recognition of digits or latin characters?
Unfortunately I think the Vision API is optimized for both ends of the spectrum -- dense text (DOCUMENT_TEXT_DETECTION) on one end, and arbitrary bits of text (TEXT_DETECTION) on the other. As you noted in the comments, the regular TEXT_DETECTION works better for these stray single digits while DOCUMENT_TEXT_DETECTION works better overall.
As far as I've heard, there are no current plans to try to cover both of these in a single way, but it's possible that this could improve in the future.
I think there have been other requests to do more fine-tuning and hinting on what you're looking to detect (e.g., here and here), but this doesn't seem to be available yet. Perhaps in the future you'll be able to provide more hints on the format of the text that you're looking to find in images (e.g., phone numbers, single digits, etc).
I have implemented a text detection algorithm in Hindi and it is working perfectly well, and have localised the textual region. Now I am looking for converting the textual regions to digital format which can be used by Google translate API. Any help regarding how I can convert it into digital format? (I have looked into SVM,ANN etc)
I would look into OCR.
The Tesseract OCR engine (open source) has language data available for many languages including Hindi, so that could be a good place to start. It can be integrated into OpenCV as well.
If you'd like to just use OpenCV then one of the Machine Learning methods (KNN, SVM) may be best, you'll need to train it yourself to recognise characters.
Few links:
https://code.google.com/p/tesseract-ocr/ (Tesseract)
https://opencv-code.com/tutorials/how-to-read-the-digits-from-a-scratchcard/ (OpenCV + Tesseract)
http://blog.damiles.com/2008/11/basic-ocr-in-opencv/ (OpenCV only - KNN)
https://opencv-code.com/tutorials/how-to-integrate-tesseract-ocr-and-opencv/ (Integrating Tesseract & OpenCV)
As the title suggest, i want to build an ANPR application in windows. I am using Brazilian number plates. And i am using OpenCV for this.
So far i manged to extract the letters form the numberplate. Following images show some of the numbers i have extracted.
The problem i am facing is that how to recognize those letter. I tried to use Google tesseract. But it fails to recognize them sometimes. Then i tried to train an OCR data base using OpenCV i used about 10 images for each character. but it also did not work properly.
So i am stuck here. i need this for final year project.So can anybody help me?? i would really appreciate it.
Following site does it very nicely
https://www.anpronline.net/demo.html
Thank you..
you could train an ann or multi-class svm on the letter images, like here
Check out OpenALPR (http://www.openalpr.com). It already has the problem solved.
If you need to do it yourself, you really do need to train Tesseract. It will give you the best results. 10 images per character is not enough, you need dozens or hundreds. If you can find a font that is similar to your plate characters, a good approach is to print out a sheet of paper with all of the characters used multiple times. Then take 5-10 pictures of the page with your camera. These can then be your input for training Tesseract.
I'm coding a physical simulation on 2d array and I'm now thinking that I could benefit from having a graphical output. My system is an array of cells (up to 2048*2048 of them) taking binary values, until now I used a prompt or text file output of '+' and '-' but it's not efficient for 2048*2048 lattice and maybe outputting in an image would be quicker and neater. Still, I've never done that. Ideally a library allowing me to write blue and red pixels/cell while parsing my lattice would get the job done. Are there some pre-existing not too long tools for doing it in c++?
Edit: I think that I just found what I was looking for: png++
After no more than 10 lines of coding I got the following output:
All I was asking for! Thank you for the suggestions ;)
You can easily get away without using an external imaging library by outputting a very simple format such as PGM or PBM. Refer to the wikipedia page on Netbpm for more details, but you're essentially outputting all the values as either ASCII or binary numbers, then any image viewer or editor that supports PGM (many of which do) can open and display them. Even if you don't have an editor, something like ImageMagick can easily convert it to a PNG or any other more accessible format.
I've used this technique in the past to quickly visualize 2D data, as you're intending to.
C++ does not have native support for graphics. You need an additional C++ library.
Personally, I suggest you to use Qt, which is free, powerful and cross-platform.
Last night before going to bed, I browsed through the Scalar Data section of Learning Perl again and came across the following sentence:
the ability to have any character in a string means you can create, scan, and manipulate raw binary data as strings.
An idea immediately hit me that I could actually let Perl scan the pictures that I have stored on my hard disk to check if they contain the string Adobe. It seems by doing so, I can tell which of them have been photoshopped. So I tried to implement the idea and came up with the following code:
#!perl
use autodie;
use strict;
use warnings;
{
local $/="\n\n";
my $dir = 'f:/TestPix/';
my #pix = glob "$dir/*";
foreach my $file (#pix) {
open my $pic,'<', "$file";
while(<$pic>) {
if (/Adobe/) {
print "$file\n";
}
}
}
}
Excitingly, the code seems to be really working and it does the job of filtering out the pictures that have been photoshopped. But problem is many pictures are edited by other utilities. I think I'm kind of stuck there. Do we have some simple but universal method to tell if a digital picture has been edited or not, something like
if (!= /the origianl format/) {...}
Or do we simply have to add more conditions? like
if (/Adobe/|/ACDSee/|/some other picture editors/)
Any ideas on this? Or am I oversimplifying due to my miserably limited programming knowledge?
Thanks, as always, for any guidance.
Your best bet in Perl is probably ExifTool. This gives you access to whatever non-image information is embedded into the image. However, as other people said, it's possible to strip this information out, of course.
I'm not going to say there is absolutely no way to detect alterations in an image, but the problem is extremely difficult.
The only person I know of who claims to have an answer is Dr. Neal Krawetz, who claims that digitally altered parts of an image will have different compression error rates from the original portions. He claims that re-saving a JPEG at different quality levels will highlight these differences.
I have not found this to be the case, in my investigations, but perhaps you might have better results.
No. There is no functional distinction between a perfectly edited image, and one which was the way it is from the start - it's all just a bag of pixels in the end, after all, and any other metadata you can remove or forge all you want.
The name of the graphics program used to edit the image is not part of the image data itself but of something called meta data - which may be stored in the image file but, as others have noted, is neither required (so some programs may not store it, some may allow you an option of not storing it) nor reliable - if you forged an image, you might have forged the meta data as well.
So the answer to your question is "no, there's no way to universally tell if the pic was edited or not, although some image editing software may write its signature into the image file and it'll be left there by carelessness of the editing person.
If you're inclined to learn more about image processing in Perl, you could take a look at some of the excellent modules CPAN has to offer:
Image::Magick - read, manipulate and write of a large number of image file formats
GD - create colour drawings using a large number of graphics primitives, and emit the drawings in various formats.
GD::Graph - create charts
GD::Graph3d - create 3D Graphs with GD and GD::Graph
However, there are other utilities available for identifying various image formats. It's more of a question for Super User, but for various unix distros you can use file to identify many different types of files, and for MacOSX, Graphic Converter has never let me down. (It was even able to open the bizarre multi-file X-ray of my cat's shattered pelvis that I got on a disc from the vet.)
How would you know what the original format was? I'm pretty sure there's no guaranteed way to tell if an image has been modified.
I can just open the file (with my favourite programming language and filesystem API) and just write whatever I want into that file willy-nilly. As long as I don't screw something up with the file format, you'd never know it happened.
Heck, I could print the image out and then scan it back in; how would you tell it from an original?
As other's have stated, there is no way to know if the image was doctored. I'm guessing what you basically want to know is the difference between a realistic photograph and one that has been enhanced or modified.
There's always the option of running some extremely complex image recognition algorithm that would analyze every pixel in your image and do some very complicated stuff to determine if the image was doctored or not. This solution would probably involve AI which would examine millions of photos that are both doctored and those that are not and learn from them. However, this is more of a theoretical solution and isn't very practical... you would probably only see it in movies. It would be extremely complex to develop and probably take years. And even if you did get something like this to work, it probably still wouldn't be 100% correct all the time. I'm guessing AI technology still isn't at that level and could take a while until it is.
A not-commonly-known feature of exiftool allows you to recognize the originating software through an analysis of the JPEG quantization tables (not relying on image metadata). It recognizes tables written by many applications. Note that some cameras may use the same quantization tables as some applications, so this isn't a 100% solution, but it is worth looking into. Here is an example of exiftool run on two images, the first was edited by photoshop.
> exiftool -jpegdigest a.jpg b.jpg
======== a.jpg
JPEG Digest : Adobe Photoshop, Quality 10
======== b.jpg
JPEG Digest : Canon EOS 30D/40D/50D/300D, Normal
2 image files read
This will work even if the metadata has been removed.
There is existing software out there which uses various techniques (compression artifacting, comparison to signature profiles in a database of cameras, etc.) to analyze the actual image data for evidence of alteration. If you have access to such software and the software available to you provides an API for external access to these analysis functions, then there's a decent chance that a Perl module exists which will interface with that API and, if no such module exists, it could probably be created rather quickly.
In theory, it would also be possible to implement the image analysis code directly in native Perl, but I'm not aware of anyone having done so and I expect that you'd be better off writing something that low-level and processor-intensive in a fully-compiled language (e.g., C/C++) rather than in Perl.
http://www.impulseadventure.com/photo/jpeg-snoop.html
is a tool that does the job almost good
If there has been any cloning , there is a variation in the pixel density..or concentration which sometimes shows up.. upon manual inspection
a Photoshop cloned area will have even pixel density(my meaning is variation of Pixels wrt a scanned image)