In order to achieve fast printed characters recognition under low price hardware implementation

a fast algorithm based on multi-stage classification for Machine Printed Character Recognition is proposed in this paper. From preprocessing

feature extraction to pattern classification

the proposed method has reasonably improved according to traditional ones. Firstly

36×36 matrix is applied for character normalization

rather than the traditional 48×48 matrix

to reduce the computation complexity for feature matching and spatial requirements for dictionary storage; Secondly

an improved coarse periphery feature with overlapping division is introduced to strengthen the stability of recognition; Thirdly

different judging criteria

including absolute distance

Euclidian distance and similarity matching

are adopted to match different requirements on recognition speed or accuracy. For 7000 samples of first class Chinese characters

the first and first five correct recognition ratio turns out to be 95 percent and 98 percent respectively in our experiments. The investigation has built a solid theory foundation to the research and development of our“Electronic Reading Pen”project.