By Ihechukwu Madubuike, Maureen Warner Lewis

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Each frame begins (or, equivalently, ends) at a synchronization blob. 0 0 0 0 Figure 2 is an example of a single frame. From left to right, the ink-dot sequence of the frame is comprised of a synchronization blob, 6 blocks, and another synchronization blob. In each block, 4 bits are encoded and thus in the frame 24 bits (0110−1010−1010−1010−0000−1100) are embedded. 2 0110 1010 1010 1010 0000 1100 Figure 3: Generation of the code for the data 10101010101010 at the frame address 01 resulting in the code of Fig.

He. A minimax classification approach to HMM-based online handwritten Chinese character recognition robust against affine distortions. In Proc. 9th International Conference on Document Analysis and Recognition (ICDAR 2007), pp. 1226–1230. IEEE Computer Society, July 2007. J. C. Woodland. Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models. Computer Speech and Language, volume 9, pp. 171–185. 1995. [11] J. Flusser and T. Suk. Character recognition by affine moment invariants.

K where tk and rk are k-th feature points of the sample to be classified and a reference sample respectively, and �·� is the Euclidean norm. This yields the affine transformation with the least distance between corresponding strokes of the input and a reference sample. This procedure is repeated for each reference sample and then distance-based classification takes place. Recognition of handwritten characters as gray scale images was proposed in [8], using similar ideas. As opposed to the first two methods described above, we propose a different technique of classifying handwritten characters with integral invariants.

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