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An Algorithm for Japanese Character Recognition
Author(s) -
Soumendu Das,
Sreeparna Banerjee
Publication year - 2014
Publication title -
international journal of image graphics and signal processing
Language(s) - English
Resource type - Journals
eISSN - 2074-9082
pISSN - 2074-9074
DOI - 10.5815/ijigsp.2015.01.02
Subject(s) - character (mathematics) , center of gravity , algorithm , centroid , euclidean distance , topology (electrical circuits) , conjunction (astronomy) , computer science , euclidean geometry , invariant (physics) , rotation (mathematics) , intersection (aeronautics) , translation (biology) , mathematics , artificial intelligence , geometry , computer vision , combinatorics , biochemistry , chemistry , physics , management , astronomy , messenger rna , economics , mathematical physics , gene , engineering , aerospace engineering
In this paper we propose a geometry- topology based algorithm for Japanese Hiragana character recognition. This algorithm is based on center of gravity identification and is size, translation and rotation invariant. In addition, to the center of gravity, topology based landmarks like conjunction points masking the intersection of closed loops and multiple strokes, as well as end points have been used to compute centers of gravity of these points located in the individual quadrants of the circles enclosing the characters. After initial pre-processing steps like notarization, resizing, cropping, noise removal, synchronization, the total number of conjunction points as well as the total number of end points are computed and stored. The character is then encircled and divided into four quadrants. The center of gravity (cog) of the entire character as well as the cogs of each of the four quadrants are computed and the Euclidean distances of the conjunction and end points in each of the quadrants with the cogs are computed and stored. Values of these quantities both for target and template images are computed and a match is made with the character having the minimum Euclidean distance. Average accuracy obtained is 94.1 %

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