An improved algorithm for segmenting and recognizing connected handwritten characters

In this paper, an improved algorithm is proposed for the segmentation and recognition of handwritten character strings. In the method, a gradient descent mechanism is used to weigh the distance measure in applying KNN for segmenting/recognizing connected characters (numerals and Chinese characters) in the left-to-right scanning direction. In recognizing connected characters, a high quality segmentation technique is essential. Conventional approaches attempt to separate the string into individual characters without recognition and apply a recognition algorithm onto each isolated character, resulting improper segmentation and poor recognition results in many situations. Our proposed algorithm simulates the human beings´s process in recognizing connected character strings where segmentation and recognition is mingled with each other. Experimental results on 1959 character strings from the USPS database of postal envelopes show that the algorithm works robustly and efficiently.