Mid-Term Summary : Part 2 - Shaping BookWorm

Approach :
I decided to tackle the problem of shirorekha in connected scripts for the first half of the summer, and the result is the WhiteWashing Algorithm. This detects the shirorekha in a script, and removes it, making it easier to separate the "akkhars" (alhabets) in a "shabda" (word).
Next, I am working on developing a matrix to help us determine the best suited filtering method for making a document OCR-ready. It will have specific instructions for a document based on it's type. An early example of the same is here.

Tools Used :

• Language : Python  2.7
• Development Environment : 
• most of the work is done on EPD 1.0.1 on Windows 7 (32-bit), using IPython notebooks.
• However, I had to Switch to Linux ( Fedora 14 on VMware workstation 9.0 ) for a while as I was unable to make  pango work on my windows.
• Notepad++ 6.3.2
• Software : 
• Tesseract 3.02 on command line (NOTE : 3.0 is available on Fedora 14, but it did not effect as I Switched to windows for the part to use Tesseract) .
• Python Libraries :
• PIL
• Scikit-Image
• Numpy
• Repository : GitHub

WhiteWashing Algo - Working

It took a bit longer than expected, but I have finally fixed the script to detect the shirorekha and whitewash over them.
I will write more about what I was doing wrong and how I fixed it later.
Sample Input :

 Sample Output : (NOTE : The black highlights show the marked coordinates)

Actual whitewashed Output :

On a Closer looks, we can see :

Here, we can see that the white-lines are too thin. Hence, next step to do for me is to find some appropriate values of padding around the coordinates when doing the whitewash.

So Long.

chop-chop

The work on the clipping code is going well. Thanks to Debayan for laying the ground-work for the algorithm.
It's complete to a point where it detects the lines as a whole, and for my convenience, prints the coordinates to a csv in a (line number, count) format. Here's an interesting glimpse :

The jump from line 66 to 124 represents a white gap, the space between the two lines.

However, it still needs some tuning. For instance, this :

the presence of 183 and 184 bothers me. It most probably represents some noise, and shows that the code still needs tuning.

But I admit, IPython notebooks are a great way to modularise and optimise when experimenting with code. It only enhances my love for Python.

Also, my algorithm is enhanced in a way as it should (in theory) be able to distinguish straight and curved lines, I'll get started on that modification after completing the basic code.

So Long.

p.s. - I'm starting to get worried now, as I think I am a bit behind the schedule I had set for myself. So push, push and chop-chop.

workaround for Pango

Having tried to make pango work on windows  for over a week now, I have finally given up, and have switched to Fedora.
On Fedora, it took a little over 3 minutes to install the dependencies and get everything up and running. more reasons to love Linux.
Anyway, the first trial of generating the images looks like this.

It is good in itself, but still needs work, and I need to synchronise the same fonts between my windows and Fedora environment, a silly mistake on my part to not take care of it already.

Coding for the clipping algorithm is about 30 % done. Right now, I am looking into connected-component analysis to see if it will work.

I plan to fix the fonts, and then work on the connected-component for the rest of the week.

So Long.

roadblock

I've hit a roadblock.
Trying to generate a new, hopefully improved, training set, I've been trying to run a script that would generate the boxfiles. But it needed 2 libraries, Cairo and Pango, to work.
I'm having trouble getting Pango to work on my windows machine.
I'll spend another day or two on it, and if it doesn't work, will have to switch.
It's going to be a long night.