Funded by: Afeyan Family Foundation
PI: Suren Khachatryan
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Project description
The past is never silent. It speaks to us in many ways, often quite literally- through handwritten manuscripts or carved stone inscriptions. Armenian monuments are particularly rich in the latter. These inscriptions serve as vital records of cultural and linguistic heritage, offering insights into the lives, beliefs and traditions of Armenians during the Middle Ages. However, reading, comprehending, and even detecting these inscriptions pose significant challenges. Due to weathering, vandalism, erosion and the complexity of ancient scripts, many of these texts remain unreadable. Yet, the few existing studies indicate that deciphering these messages from the past is feasible with technological advancements. The modern inscription recognition methods are dominated by a two-phase approach- a character detection followed by their classification. The team studied a unique, newly created and unexplored collection of digital twins of Armenian tapanakars and khachkars and processed both 2D images and full 3D models with an aim to improve the detection accuracy. The hierarchical segmentation was implemented at three levels using the detected geometrical and statistical features- multi-line texts, individual text lines and characters or strokes in the text line. Next, the team applied the results to character classification and estimated the accuracy of the generated character images. Finally, they discussed the extensions and further developments. Since the detection stage of the algorithm is universal for any kind of shapes, it opens up new research avenues that extend beyond text recognition alone. The same pipeline can be adapted to identify decorative motifs, geometric symbols and other visual patterns commonly found on tapanakar surfaces.
