This paper uses a modified Hough transform method to extract features from the cursive handwritten digit and characters CEDAR data. The technique does not require the detection of complex structural primitives such as loops etc. The handwriting images are divided into uniform regions that are analyzed for the presence of horizontal, vertical and diagonal segments. The total number of such segments found in these regions is used as input to a linear classifier (discriminant analysis) and a non-linear classifier (nearest neighbor). The results are produced on the complete test sets specified in CEDAR database as well as a leave-one-out cross-validation is performed. On the digit data, the results show a recognition rate of around 94% correct recognition on the test set and 87.5% using a leave-one-out method. Character recognition results range between 67% correct on test set and 64% correct using leave-one-out cross-validation.