Traditional literature considers complex biometric sources such as words, letters and signatures for writer verification/identification. In this work the use of small segments of the handwritten stroke for writer verification is proposed. A grapheme is defined as the concatenation of smaller segments or fragments. Two models of grapheme are developed based on the idea that the segments are parts of a circle with or without direction. The average of Gray Level of the Perpendicular Line to the Skeleton and Local Binary Pattern are adopted as descriptors. A database of 3,000 images of 50 writers, with 6 types of segments and with 10 samples per segment has been developed. A binary output support vector machine was applied as classifier. Thus, 50 classifiers were trained using 100 balanced data sets generated using subsampling of the majority class. Experiments are carried out with the proposed models, with an identity verification hit rate of 97 % on average. The positive results of experiments show that it is possible to achieve high precision in writer verification using segment of graphemes.