Is it possible by using the Zbar API, that one can check if the image consists of barcode or not?
This is as a backup measure, so that if the application is unable to get barcode value, let it check if it might contain a barcode, if so user can later manually verify it.
I have explored quite a bit but with no major success. If not ZBar, any other open source library that can do it well?
Thanks
What you need is a detector, i.e. the ability to locate the barcode (if any), and thus just return yes or no according to the detection result.
IMHO Zbar does not provide a versatile enough API to do so since it exposes a high-level scanner interface (zbar_scan_image) that combines detection & decoding on one hand, and a pure decoder interface on the other hand.
You should definitely refer to this paper: Robust 1D Barcode Recognition on Mobile Devices. It contains an entire section related to the detection step including pseudo-algorithms [1] - see 4. Locating the barcode. But there is no ready-to-use open source library: you would have to implement your own detector based on the described techniques.
At last, more pragmatic/simple techniques may be used depending on the kind of input images you plan to work with (is there any rotation? blur? is it about processing images or the video stream in real-time?).
[1] In addition I would say that it's a good idea to use a different kind of algorithm within this fallback step than the one used within the first step.
Related
I am working on a feature matching project and i am using OpenCV Python as the tool for developed the application.
According to the project requirement, my database have images of some objects like glass, ball,etc ....with their descriptions. User can send images to the back end of the application and back end is responsible for matching the sent image with images which are exist in the database and send the image description to the user.
I had done some research on the above scenario. Unfortunately still i could not find a algorithm for matching two images and identifying both are matching or not.
If any body have that kind of algorithm please send me.(I have to use OpenCV python or JavaCV)
Thank you
This is a very common problem in Computer Vision nowadays. A simple solution is really simple. But there are many, many variants for more sophisticated solutions.
Simple Solution
Feature Detector and Descriptor based.
The idea here being that you get a bunch of keypoints and their descriptors (search for SIFT/SURF/ORB). You can then find matches easily with tools provided in OpenCV. You would match the keypoints in your query image against all keypoints in the training dataset. Because of typical outliers, you would like to add a robust matching technique, like RanSaC. All of this is part of OpenCV.
Bag-of-Word model
If you want just the image that is as much the same as your query image, you can use Nearest-Neighbour search. Be aware that OpenCV comes with the much faster Approximated-Nearest-Neighbour (ANN) algorithm. Or you can use the BruteForceMatcher.
Advanced Solution
If you have many images (many==1 Million), you can look at Locality-Sensitive-Hashing (see Dean et al, 100,000 Object Categories).
If you do use Bag-of-Visual-Words, then you should probably build an Inverted Index.
Have a look at Fisher Vectors for improved accuracy as compared to BOW.
Suggestion
Start by using Bag-Of-Visual-Words. There are tutorials on how to train the dictionary for this
model.
Training:
Extract Local features (just pick SIFT, you can easily change this as OpenCV is very modular) from a subset of your training images. First detect features and then extract them. There are many tutorials on the web about this.
Train Dictionary. Helpful documentation with a reference to a sample implementation in Python (opencv_source_code/samples/python2/find_obj.py)!
Compute Histogram for each training image. (Also in the BOW documentation from previous step)
Put your image descriptors from the step above into a FLANN-Based-matcher.
Querying:
Compute features on your query image.
Use the dictionary from training to build a BOW histogram for your query image.
Use that feature to find the nearest neighbor(s).
I think you are talking about Content Based Image Retrieval
There are many research paper available on Internet.Get any one of them and Implement Best out of them according to your needs.Select Criteria according to your application like Texture based,color based,shape based image retrieval (This is best when you are working with image retrieval on internet for speed).
So you Need python Implementation, I would like to suggest you to go through Chapter 7, 8 of book Computer Vision Book . It Contains Working Example with code of what you are looking for
One question you may found useful : Are there any API's that'll let me search by image?
Would OCR Software be able to reliably translate an image such as the following into a list of values?
UPDATE:
In more detail the task is as follows:
We have a client application, where the user can open a report. This report contains a table of values.
But not every report looks the same - different fonts, different spacing, different colors, maybe the report contains many tables with different number of rows/columns...
The user selects an area of the report which contains a table. Using the mouse.
Now we want to convert the selected table into values - using our OCR tool.
At the time when the user selects the rectangular area I can ask for extra information
to help with the OCR process, and ask for confirmation that the values have been correct recognised.
It will initially be an experimental project, and therefore most likely with an OpenSource OCR tool - or at least one that does not cost any money for experimental purposes.
Simple answer is YES, you should just choose right tools.
I don't know if open source can ever get close to 100% accuracy on those images, but based on the answers here probably yes, if you spend some time on training and solve table analisys problem and stuff like that.
When we talk about commertial OCR like ABBYY or other, it will provide you 99%+ accuracy out of the box and it will detect tables automatically. No training, no anything, just works. Drawback is that you have to pay for it $$. Some would object that for open source you pay your time to set it up and mantain - but everyone decides for himself here.
However if we talk about commertial tools, there is more choice actually. And it depends on what you want. Boxed products like FineReader are actually targeting on converting input documents into editable documents like Word or Excell. Since you want actually to get data, not the Word document, you may need to look into different product category - Data Capture, which is essentially OCR plus some additional logic to find necessary data on the page. In case of invoice it could be Company name, Total amount, Due Date, Line items in the table, etc.
Data Capture is complicated subject and requires some learning, but being properly used can give quaranteed accuracy when capturing data from the documents. It is using different rules for data cross-check, database lookups, etc. When necessary it may send datafor manual verification. Enterprises are widely usind Data Capture applicaitons to enter millions of documents every month and heavily rely on data extracted in their every day workflow.
And there are also OCR SDK ofcourse, that will give you API access to recognition results and you will be able to program what to do with the data.
If you describe your task in more detail I can provide you with advice what direction is easier to go.
UPDATE
So what you do is basically Data Capture application, but not fully automated, using so-called "click to index" approach. There is number of applications like that on the market: you scan images and operator clicks on the text on the image (or draws rectangle around it) and then populates fields to database. It is good approach when number of images to process is relatively small, and manual workload is not big enough to justify cost of fully automated application (yes, there are fully automated systems that can do images with different font, spacing, layout, number of rows in the tables and so on).
If you decided to develop stuff and instead of buying, then all you need here is to chose OCR SDK. All UI you are going to write yoursself, right? The big choice is to decide: open source or commercial.
Best Open source is tesseract OCR, as far as I know. It is free, but may have real problems with table analysis, but with manual zoning approach this should not be the problem. As to OCR accuracty - people are often train OCR for font to increase accuracy, but this should not be the case for you, since fonts could be different. So you can just try tesseract out and see what accuracy you will get - this will influence amount of manual work to correct it.
Commertial OCR will give higher accuracy but will cost you money. I think you should anyway take a look to see if it worth it, or tesserack is good enough for you. I think the simplest way would be to download trial version of some box OCR prouct like FineReader. You will get good idea what accuracy would be in OCR SDK then.
If you always have solid borders in your table, you can try this solution:
Locate the horizontal and vertical lines on each page (long runs of
black pixels)
Segment the image into cells using the line coordinates
Clean up each cell (remove borders, threshold to black and white)
Perform OCR on each cell
Assemble results into a 2D array
Else your document have a borderless table, you can try to follow this line:
Optical Character Recognition is pretty amazing stuff, but it isn’t
always perfect. To get the best possible results, it helps to use the
cleanest input you can. In my initial experiments, I found that
performing OCR on the entire document actually worked pretty well as
long as I removed the cell borders (long horizontal and vertical
lines). However, the software compressed all whitespace into a single
empty space. Since my input documents had multiple columns with
several words in each column, the cell boundaries were getting lost.
Retaining the relationship between cells was very important, so one
possible solution was to draw a unique character, like “^” on each
cell boundary – something the OCR would still recognize and that I
could use later to split the resulting strings.
I found all this information in this link, asking Google "OCR to table". The author published a full algorithm using Python and Tesseract, both opensource solutions!
If you want to try the Tesseract power, maybe you should try this site:
http://www.free-ocr.com/
Which OCR you are talking about?
Will you be developing codes based on that OCR or you will be using something off the shelves?
FYI:
Tesseract OCR
it has implemented the document reading executable, so you can feed the whole page in, and it will extract characters for you. It recognizes blank spaces pretty well, it might be able to help with tab-spacing.
I've been OCR'ing scanned documents since '98. This is a recurring problem for scanned docs, specially for those that include rotated and/or skewed pages.
Yes, there are several good commercial systems and some could provide, once well configured, terrific automatic data-mining rate, asking for the operator's help only for those very degraded fields. If I were you, I'd rely on some of them.
If commercial choices threat your budget, OSS can lend a hand. But, "there's no free lunch". So, you'll have to rely on a bunch of tailor-made scripts to scaffold an affordable solution to process your bunch of docs. Fortunately, you are not alone. In fact, past last decades, many people have been dealing with this. So, IMHO, the best and concise answer for this question is provided by this article:
https://datascience.blog.wzb.eu/2017/02/16/data-mining-ocr-pdfs-using-pdftabextract-to-liberate-tabular-data-from-scanned-documents/
Its reading is worth! The author offers useful tools of his own, but the article's conclusion is very important to give you a good mindset about how to solve this kind of problem.
"There is no silver bullet."
(Fred Brooks, The Mitical Man-Month)
It really depends on implementation.
There are a few parameters that affect the OCR's ability to recognize:
1. How well the OCR is trained - the size and quality of the examples database
2. How well it is trained to detect "garbage" (besides knowing what's a letter, you need to know what is NOT a letter).
3. The OCR's design and type
4. If it's a Nerural Network, the Nerural Network structure affects its ability to learn and "decide".
So, if you're not making one of your own, it's just a matter of testing different kinds until you find one that fits.
You could try other approach. With tesseract (or other OCRS) you can get coordinates for each word. Then you can try to group those words by vercital and horizontal coordinates to get rows/columns. For example to tell a difference between a white space and tab space. It takes some practice to get good results but it is possible. With this method you can detect tables even if the tables use invisible separators - no lines. The word coordinates are solid base for table recog
We also have struggled with the issue of recognizing text within tables. There are two solutions which do it out of the box, ABBYY Recognition Server and ABBYY FlexiCapture. Rec Server is a server-based, high volume OCR tool designed for conversion of large volumes of documents to a searchable format. Although it is available with an API for those types of uses we recommend FlexiCapture. FlexiCapture gives low level control over extraction of data from within table formats including automatic detection of table items on a page. It is available in a full API version without a front end, or the off the shelf version that we market. Reach out to me if you want to know more.
Here are the basic steps that have worked for me. Tools needed include Tesseract, Python, OpenCV, and ImageMagick if you need to do any rotation of images to correct skew.
Use Tesseract to detect rotation and ImageMagick mogrify to fix it.
Use OpenCV to find and extract tables.
Use OpenCV to find and extract each cell from the table.
Use OpenCV to crop and clean up each cell so that there is no noise that will confuse OCR software.
Use Tesseract to OCR each cell.
Combine the extracted text of each cell into the format you need.
The code for each of these steps is extensive, but if you want to use a python package, it's as simple as the following.
pip3 install table_ocr
python3 -m table_ocr.demo https://raw.githubusercontent.com/eihli/image-table-ocr/master/resources/test_data/simple.png
That package and demo module will turn the following table into CSV output.
Cell,Format,Formula
B4,Percentage,None
C4,General,None
D4,Accounting,None
E4,Currency,"=PMT(B4/12,C4,D4)"
F4,Currency,=E4*C4
If you need to make any changes to get the code to work for table borders with different widths, there are extensive notes at https://eihli.github.io/image-table-ocr/pdf_table_extraction_and_ocr.html
I am working on a project where I am have image files that have been malformed (fuzzed i.e their image data have been altered). These files when rendered on various platforms lead to warning/crash/pass report from the platform.
I am trying to build a shield using unsupervised machine learning that will help me identify/classify these images as malicious or not. I have the binary data of these files, but I have no clue of what featureSet/patterns I can identify from this, because visually these images could be anything. (I need to be able to find feature set from the binary data)
I need some advise on the tools/methods I could use for automatic feature extraction from this binary data; feature sets which I can use with unsupervised learning algorithms such as Kohenen's SOM etc.
I am new to this, any help would be great!
I do not think this is feasible.
The problem is that these are old exploits, and training on them will not tell you much about future exploits. Because this is an extremely unbalanced problem: no exploit uses the same thing as another. So even if you generate multiple files of the same type, you will in the end have likely a relevant single training case for example for each exploit.
Nevertheless, what you need to do is to extract features from the file meta data. This is where the exploits are, not in the actual image. As such, parsing the files is already much the area where the problem is, and your detection tool may become vulnerable to exactly such an exploit.
As the data may be compressed, a naive binary feature thing will not work, either.
You probably don't want to look at the actual pixel data at all since the corruption most (almost certain) lay in the file header with it's different "chunks" (example for png, works differently but in the same way for other formats):
http://en.wikipedia.org/wiki/Portable_Network_Graphics#File_header
It should be straight forward to choose features, make a program that reads all the header information from the file and if the information is missing and use this information as features. Still will be much smaller then the unnecessary raw image data.
Oh, and always start out with simpler algorithms like pca together with kmeans or something, and if they fail you should bring out the big guns.
I would like to know what algorithm is used to obtain an image and get the objects present in the image and process (give information about) it. And also, how is this done?
I agree with Sid Farkus, there is no simple answer to this question.
Maybe you can get started by checking out the Open Computer Vision Library. There is a Wiki page on object detection with links to a How-To and to papers.
You may find other examples and approaches (i.e. algorithms); it's likely that the algorithms differ by application (i.e. depending on what you actually want to detect).
There are many ways to do Object Detection and it still an open problem.
You can start with template matching, It is probably the simplest way to solve, which consists of making a convolution with the known image (IA) on the new image (IB). It is a fairly simple idea because it is like applying a filter on the signal, the filter will generate a maximum point in the image when it finds the object, as shown in the video. But that technique has several cons, does not handle variants in scale or rotation so it has no real application.
Also you can find another option more robust feature matching, which consist on create a dataset with features such as SIFT, SURF or ORB of different objects with this you can train a SVM to recognize objects
You can also check deformable part models. However, The state of the art object detection is based on deep-learning such as Faster R-CNN, Alexnet, which learn the features that will be used to detect/recognize the objects
Well this is hardly an answerable question, but for most computer vision applications a good starting point is the Hough Transform
This is a fairly broad question; what tools/libraries exist to take two photographs that are not identical, but extremely similar, and identify the specific differences between them?
An example would be to take a picture of my couch on Friday after my girlfriend is done cleaning and before a long weekend of having friends over, drinking, and playing rock band. Two days later I take a second photo of the couch; lighting is identical, the couch hasn't moved a milimeter, and I use a tripod in a fixed location.
What tools could I use to generate a diff of the images, or a third heatmap image of the differences? Are there any tools for .NET?
This depends largely on the image format and compression. But, at the end of the day, you are probably taking two rasters and comparing them pixel by pixel.
Take a look at the Perceptual Image Difference Utility.
The most obvious way to see every tiny, normally nigh-imperceptible difference, would be to XOR the pixel data. If the lighting is even slightly different, though, it might be too much. Differencing (subtracting) the pixel data might be more what you're looking for, depending on how subtle the differences are.
One place to start is with a rich image processing library such as IM. You can dabble with its operators interactively with the IMlab tool, call it directly from C or C++, or use its really decent Lua binding to drive it from Lua. It supports a wide array of operations on bitmaps, as well as an extensible library of file formats.
Even if you haven't deliberately moved anything, you might want to use an algorithm such as SIFT to get good sub-pixel quality alignment between the frames. Unless you want to treat the camera as fixed and detect motion of the couch as well.
I wrote this free .NET application using the toolkit my company makes (DotImage). It has a very simple algorithm, but the code is open source if you want to play with it -- you could adapt the algorithm to .NET Image classes if you don't want to buy a copy of DotImage.
http://www.atalasoft.com/cs/blogs/31appsin31days/archive/2008/05/13/image-difference-utility.aspx
Check out Andrew Kirillov's article on CodeProject. He wrote a C# application using the AForge.NET computer vision library to detect motion. On the AForge.NET website, there's a discussion of two frame differences for motion detection.
It's an interesting question. I can't refer you to any specific libraries, but the process you're asking about is basically a minimal case of motion compensation. This is the way that MPEG (MP4, DIVX, whatever) video manages to compress video so extremely well; you might look into MPEG for some information about the way those motion compensation algorithms are implemented.
One other thing to keep in mind; JPEG compression is a block-based compression; much of the benefit that MPEG brings from things is to actually do a block comparison. If most of your image (say the background) is the same from one image to the next, those blocks will be unchanged. It's a quick way to reduce the amount of data needed to be compared.
just use the .net imaging classes, create a new bitmap() x 2 and look at the R & G & B values of each pixel, you can also look at the A (Alpha/transparency) values if you want to when determining difference.
also a note, using the getPixel(y, x) method can be vastly slow, there is another way to get the entire image (less elegant) and for each ing through it yourself if i remember it was called the getBitmap or something similar, look in the imaging/bitmap classes & read some tutes they really are all you need & aren't that difficult to use, dont go third party unless you have to.