Hand-written text recognition based on a new formulation

The hand-written word recognition problem is formulated in two steps. In the first step, the plausibility of observing each character is computed as a function of sample index of a line. In the second step, word recognition is achieved by finding the word (sequence of characters) which maximizes the sum of plausibilities of individual characters which make up the word. The authors propose an efficient algorithm for the second step which makes use of peaks of plausibility functions and solves the maximization process by two embedded search processes: finding the best path connecting peaks of the plausibility functions of two successive characters, and finding the best transition sample index for two given peaks. In a preliminary experimentation, using template matching for character plausibility estimation, a 96% recognition rate was obtained for a cursive-writing-like font