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Real-Time Multi Oriented Ancient Script Recognition using Single Layer Hierarchical Graph Neuron (SLHGN)

Ancient scripts are not only multi oriented, but they also require enormous efforts to be recognized in real-time manner. This problem is typical for Single Layer Hierarchical Graph Neuron (SLHGN) to be solved. The SLHGN is already known that it implements a single cycle memorization and recall operation. The scheme also utilizes small response time that is insensitive to the increases in the number of stored patterns. In this improved approach, the architecture is not only suitable for scrutinizing incomplete patterns, it can also accommodate multi oriented patterns. As the result, the architecture is able to recognize patterns that are not at the same orientation. Such capability can contribute to recognize ancient scripts that are multi oriented, yet the same, patterns. Since SLHGN architecture still encompasses a lightweight in-network processing algorithm which does not require expensive floating point computations, it is suitable for such a real-time process.