With the PDF below, I would like to do the following things.
Localize the four sudoku grids so as to treat each of them separately.
For each grid picture, I would like to obtain a matrix of the pictures corresponding to each cell.
Finally, I would like to "find" the values printed in each cell.
The problem is that I'm a real beginner with OpenCV (I've bought a book about OpenCV with Python but I've not received it yet).
I'm not a beginner in Python, neither in math so every clue is welcomed.
You're in luck:
sudoku solver part 1
part 2
part 3
part 4
Python 3.x isn't supported by OpenCV though.
tesseract has nice python bindings, too (and is more specialized on that 'ocr' job ;)
welcome to opencv, though !
Related
I saw how to do this from a DataSet object, and I saw a setLabel method, and I saw a getLabelMaskArrays, but none of these are what I'm looking for.
Am I just blind or is there not a way?
Thanks
Masking is for variable length time series in RNNs. Most of the time you don't need it. Our built in sequence dataset iterators also tend to handle these cases. For more details see our rnn page: https://deeplearning4j.org/usingrnns
I often work with scanned papers. The papers contain tables (similar to Excel tables) which I need to type into the computer manually. To make the task worse the tables can be of different number of columns. Manually entering them into Excel is mundane to say the least.
I thought I can save myself a week of work if I can put a program to OCR it. Would it be possible to detect headers text areas with the OpenCV and OCR the text behind the detected image coordinates.
Can I achieve this with the help of OpenCV or do I need entirely different approach?
Edit: Example table is really just a standard table similar to what you can see in Excel and other spread-sheet applications, see below.
This question seems a little old but i was also working on a similar problem and got my own solution which i am explaining here.
For reading text using any OCR engine there are many challanges in getting good accuracy which includes following main cases:
Presence of noise due to poor image quality / unwanted elements/blobs in the background region. This will require some pre-processing like noise removal which can be easily done using gaussian filter or normal median filter methods. These are also available in opencv.
Wrong orientation of image: Because of wrong orientation OCR engine fails to segment the lines and words in image correctly which gives the worst accuracy.
Presence of lines: While doing word or line segmentation OCR engine sometimes also tries to merge the words and lines together and thus processing wrong content and hence giving wrong results.
There are other issues also but these are the basic ones.
In this case i think the scan image quality is quite good and simple and following steps can be used solve the problem.
Simple image binarization will remove the background content leaving only necessary content as shown here.
Now we have to remove lines which in this case is tabular grid. This can also be identified using connected components and removing the large connected components. So our final image that is needed to be fed to OCR engine will look like this.
For OCR we can use Tesseract Open Source OCR Engine. I got following results from OCR:
Caption title
header! header2 header3
row1cell1 row1cell2 row1cell3
row2cell1 row2cell2 row2cell3
As we can see here that result is quite accurate but there are some issues like
header! which should be header1, this is because OCR engine misunderstood ! with 1. This problem can be solved by further processing the result using Regex based operations.
After post processing the OCR result it can be parsed to read the row and column values.
Also here in this case to classify the sheet title, heading and normal cell values their font information can be used.
I'm trying to do some algorithm comparison for plagiarism. I've found many TEXT comparison for plagiarism.
But in an algorithm it's very different. Let's say that some algorithm uses an huge number of variables, functions and user defined structures. If some guy copy the source code from someone, he'll at least, change the variables and functions names. With an simple text comparison algorithm this difference in functions and variables letters will count as an "difference" making the algorithm gives an "false" for plagiarism.
What I want to do is "generalize" (I don't know if that's the right word) all the variables, functions and user-defined structures names in an C++ source code. So the varibles will be named like "a", "b", the same for functions "... fa(...)", "... fb(...)".
I have the c++ source algorithms in strings variables in PHP to be compared.
I know that many other things should be analysed for an accurate source code comparison, but that will be enough to me.
It's an interesting question. Depending on how complex the algorithm, however, it might be that variable names are what gives the plagiarism away. How many ways can you really code up a tree traversal for example?
I think there was a paper a few years ago on identifying coders through their style - looking at all the little things like whitespace, where {}s are placed, etc. Who knows but maybe that is the way to go, look for a negative match to the student's previous style rather than positive match to the known sources. Saying that, students aren't likely to have developed a very personal coding style at an early stage of learning.
One thought - what language are the examples written in? Can it be compiled? If you compile C and then do a binary comparison on the executables, then will identical programs with different local variable names have the exact same binary? (Global vars and functions wouldn't, though).
I've used MOSS in the past: http://theory.stanford.edu/~aiken/moss/ to detect plagiarized code. Since it works on a semantic level, it will detect the situations you presented above. The tool is language-aware, so comments are not considered in the analysis, and it goes a long way in detecting code that has been modified through simple search-and-replace of variable and/or function names.
Note: I used the tool a few years ago when I taught computer science in grad school, and it worked wonderfully in detecting code that had been yanked from the internet. Here is a well-documented account of similar application: http://fie2012.org/sites/fie2012.org/history/fie99/papers/1110.pdf
If you google "measure software similarity", you should find a few more useful hits: http://www.ics.heacademy.ac.uk/resources/assessment/plagiarism/detectiontools_sourcecode.html
I tried tikz/pgf a bit but have not had much luck creating a nice diagram to visualize bitfields or byte fields of packed data structures (i.e. in memory). Essentially I want a set of rectangles representing ranges of bits with labels inside, and offsets along the top. There should be multiple rows for each word of the data structure. This is similar to most of the diagrams in most processor manuals labeling opcode encoding etc.
Has anyone else tried to do this using latex or is there a package for this?
I have successfully used the bytefield package for something like this. If it doesn't do exactly what you want, please extend your question with an example...
You will find several examples with both tikz code source and a visual rendering of this code at http://www.texample.net/tikz/examples/
I am using FANN, pyfann in particular, for signature recognition. Before I can use AI, I have to prepocess the image first using the imagelab, a compilation of image processing libraries like image.h,jpegio.h,etc. My problem is I don't know how to incorporate the two so that I can use the their libraries in just one program. I have to extract the signatures' features like no. of pixels and length and width, but I don't know how to input these data to FANN. Any help? I really don't know exactly where to start.