Verification and/or identification (VI) of the individual writer-identity is one of the most common secure personal biometric authentications, particularly in banks for verification and in sensitive data storages for both VI. A writer-identity VI system (WIVIS) is proposed using the writer-invariant features of his/her script using two approaches: offline approaches, which rely on the script information in a static format, such as an image or shape, and online approaches, which require the collection of information in a dynamic format, such as speed and acceleration, using a tablet with a stylus pen to capture both of these dynamic information. Both offline script VI methods, such as normalized Fourier transform descriptor (NFTD) and normalized central moment (NCM), and online script VI methods, such as normalized script speed and acceleration, will be discussed. These features are compared individually and then as a combination. In the combination mode, the neural network (NN) is used for classification. Implementation and testing of the WIVIS is done and analyzed, and the effectiveness of each invariant algorithm, regardless of the language or form of the script shape, is discussed. A set of data on the online (dynamic) and offline (static) script is also discussed.