Handwritten word recognition based on geometric decomposition

A method for classifying an image representation of a handwritten word of cursive script, said method comprising:

• optically reading a handwritten word so as to form an image representation thereof comprising a bit map of pixels;
• extracting a pixel contour of said bit map;
• detecting vertical peak and minima pixel extrema on upper and lower zones of said contour respectively;
• organizing said peak and minima pixel extrema into respective independent peak and minima sequences,
• determining the respective feature vectors of said peak and minima sequences;
• and
• classifying said word image according to said peak and minima feature vectors.

An apparatus for recognizing a handwritten word of cursive script, said apparatus comprising

• a template of words having been previously classified according to feature vectors,
• a reader for optically reading a handwritten word, and
• a controller being linked to said template and said reader;
• wherein said controller:
• forms an image representation of said handwritten word comprising a bit map of pixels;
• extracts a pixel contour of said bit map;
• detects vertical peak and minima pixel extrema on upper and lower zones of said contour respectively;
• determines respective feature vectors of said peak and minima sequences;
• and compares said feature vectors with said template so as to generate a match between said handwritten word and a said previously classified word.

A method for correcting a slant of an image representation of a handwritten word of cursive script comprising a bit map of pixels, said method comprising:

• pre-computing a look-up table of the image representation that maps Cartesian coordinates to polar coordinates;
• performing a Radon transform in the polar domain of the image representation;
• and
• calculating a slant angle from the Radon transform in the polar domain.