OCR and character similarity

Question

I am currently working on some kind of OCR (Optical Character Recognition) system. I have already written a script to extract each character from the text and clean (most of the) irregularities out of it. I also know the font. The images I have now for example are:

M (http://i.imgur.com/oRfSOsJ.png (font) and http://i.imgur.com/UDEJZyV.png (scanned))

K (http://i.imgur.com/PluXtDz.png (font) and http://i.imgur.com/TRuDXSx.png (scanned))

C (http://i.imgur.com/wggsX6M.png (font) and http://i.imgur.com/GF9vClh.png (scanned))

For all of these images I already have a sort of binary matrix (1 for black, 0 for white). I was now wondering if there was some kind of mathematical projection-like formula to see the similarity between these matrices. I do not want to rely on a library, because that was not the task given to me.

I know this question may seem a bit vague and there are similar questions, but I'm looking for the method, not for a package and so far I couldn't find any comments regarding the method. The reason this question being vague is that I really have no point to start. What I want to do is actually described here on wikipedia:

Matrix matching involves comparing an image to a stored glyph on a pixel-by-pixel basis; it is also known as "pattern matching" or "pattern recognition".[9] This relies on the input glyph being correctly isolated from the rest of the image, and on the stored glyph being in a similar font and at the same scale. This technique works best with typewritten text and does not work well when new fonts are encountered. This is the technique the early physical photocell-based OCR implemented, rather directly. (http://en.wikipedia.org/wiki/Optical_character_recognition#Character_recognition)

If anyone could help me out on this one, I would appreciate it very much.

Answer 1

for recognition or classification most OCR's use neural networks

These must be properly configured to desired task like number of layers internal interconnection architecture , and so on. Also problem with neural networks is that they must be properly trained which is pretty hard to do properly because you will need to know for that things like proper training dataset size (so it contains enough information and do not over-train it). If you do not have experience with neural networks do not go this way if you need to implement it yourself !!!

There are also other ways to compare patterns

1. vector approach

   • polygonize image (edges or border)
   • compare polygons similarity (surface area, perimeter, shape ,....)

2. pixel approach

   You can compare images based on:

   • histogram
   • DFT/DCT spectral analysis
   • size
   • number of occupied pixels per each line
   • start position of occupied pixel in each line (from left)
   • end position of occupied pixel in each line (from righ)
   • these 3 parameters can be done also for rows
   • points of interest list (points where is some change like intensity bump,edge,...)

   You create feature list for each tested character and compare it to your font and then the closest match is your character. Also these feature list can be scaled to some fixed size (like 64x64) so the recognition became invariant on scaling.

   Here is sample of features I use for OCR

   

   In this case (the feature size is scaled to fit in NxN) so each character has 6 arrays by N numbers like:

   int row_pixels[N]; // 1nd image
   int lin_pixels[N]; // 2st image
   int row_y0[N];     // 3th image green
   int row_y1[N];     // 3th image red
   int lin_x0[N];     // 4th image green
   int lin_x1[N];     // 4th image red
   

   Now: pre-compute all features for each character in your font and for each readed character. Find the most close match from font

   • min distance between all feature vectors/arrays
   • not exceeding some treshold difference

   This is partialy invariant on rotation and skew up to a point. I do OCR for filled characters so for outlined font it may have use some tweaking

[Notes]

For comparison you can use distance or correlation coefficient