This paper is presenting a handwriting strokes and grapheme-based offline writer identification framework. This framework works by firstly measuring the hand pressures during script writing using identical grapheme and writing strokes and then generates the pressure descriptors which are rotation as well as scaleinvariant. The descriptors are used to present different hand pressure distribution accuracies which are defined according to approximation-coefficients of the grapheme zone, perpendicular lines average over the handwritten script skeleton, stroke-width, and handwritten script skeleton grapheme. Discrete-Cosine Transform and Principal- Component-Analysis methods are used to evaluate the descriptors execution accuracy. The performance of the proposed method is assessed with the help of one-versus-all strategy and the k-fold validation is done with the help of Structural Support Vector Machine (S-SVM). Whereas heuristic enhancement calculation based simulated annealing is used to identify the S-SVM hyper parameters. The performance assessment of the handwriting strokes and grapheme based offline writer identification framework with single character gives the encouraging results. Also the combination of the characters enhances the accuracy as well as overall performance of personality identification up to 99.99%.