The Heuristic extraction algorithms for freeman chain code of handwritten character

Abstract

Handwriting character recognition (HCR) is the ability of a computer to receive and interpret handwritten input. In HCR, there are many representation schemes and one of them is Freeman’s chain code (FCC). Chain code is a sequence of code directions of a character and connection to a starting point which is often used in image processing. The main problem in representing a character using FCC is that the results of the extractions depend on the starting points. Unfortunately, the studies about FCC extraction using on e continuous route and to minimize the length of chain code to FCC from a thinned binary image (TBI) have not been widely explored. To solve this problem, heuristic algorithms are proposed to extract the FCC to correctly represent the characters. This paper proposes two heuristics algorithms that are based on randomized and enumeration algorithms to solve the problems. As a problem solving technique, the randomized-based algorithm makes the random choices while the enumeration-based algorithm enumerates all possible candidates for a solution. The performance measurements of the algorithms are the route lengths and computation times. The experiments on the algorithms are performed based on the chain code representation derived from established previous works of Center of Excellence for Document Analysis and Recognition (CEDAR) dataset which consists of 126 upper-case letter characters. The experimental results show that the route lengths for both algorithms are similar but the computation times of the enumeration-based algorithm is higher than the randomized-based algorithm. This is because the enumeration-based algorithm considers all branches in route walk.