Recognition of inscriptions in ancient Sri Lanka

Recognizing the content in the ancient inscriptions unlocks many gateways to the undiscovered historical events since inscriptions were used as a major communication mechanism in ancient Sri Lanka. Currently these inscriptions are read through naked human eye with a great effort. This manual process is not only time consuming but also can generates uncertain outputs sometimes due to the noise that is available in the inscriptions. We hypnotize that the noise removal of a textual document can be resolved through communication among lexical, structure analyst and semantic agents of a multi agent solution. This is inspired by the real world scenario where noisy outputs can be resolved by experts through their knowledge in morphology, sentence structure and semantics of a particular context. This thesis is an attempt to recognize the Brahmi characters in ancient Sri Lankan inscriptions. The overall solution comprises of several agents namely: artificial neural network agent, lexical agent, structure analyst agent and semantic agent. The input for the proposed system is an ancient Sri Lankan inscription, this particular inscription image is pre processed using different image processing techniques and segmented into isolated characters. The artificial neural network agent analyzes the pixel intensity of the isolated characters, extract the features and recognize the relevant Brahmi character using the trained neural network. The recognized character string could contain Brahmi characters which have identified erroneously due to the high noise availability. The lexical, structure analyst agent and semantic agents plays a major role to correct the mistakenly identified characters by communicating with each other. The output of the system consists of relevant Sinhala Unicode characters for the recognized Brahmi character string. Experiments were carried out to evaluate the recognition rate of the system by using 12 inscriptions that were found in archaeological sites – Wessagiriya , Handagala Vihara etc. 84% of inscriptions were completely recognized and among the rest 8% of inscriptions were partially identified