Accurate off-line character recognition is still very important for camera based printed document analysis. Due to its inherent conceptual and technical simplicity, conventional recognition strategies relied on features extracted using square block zoning of a character image. In this paper, we propose an isotropic feature extraction method using regular hexagonal zoning and empirically confirm its effectiveness for printed and handwritten character recognition. We accomplished printed character recognition and handwritten character recognition experiments using large-scale evaluating datasets. The average accuracy was improved by 2 % in experiments using gradient features. And the effectiveness of hexagonal zoning for recognition of high stroke count characters and low-resolution characters is confirmed in both printed and handwritten character recognition by the experiments.