Optimization of Incung Ancient Manuscript Character Recognition Using Template Matching and Image Enhancement
DOI:
https://doi.org/10.31849/digitalzone.v17i1.29838Keywords:
Ancient Manuscripts, Smart City, Enhancement, Template Matching, CNN, CultureAbstract
Ancient manuscripts represent valuable cultural heritage but are highly vulnerable to physical degradation and loss of public understanding. One such heritage is the Incung script manuscripts from Kerinci Regency, Indonesia, which remain underexplored in digital recognition research. This study proposes an optimized integration of image enhancement, template matching, and Convolutional Neural Network (CNN) methods to improve the accuracy and stability of Incung character recognition. Image enhancement is applied to improve contrast and legibility, template matching is used to capture structural character patterns, and CNN is employed as a complementary classifier to validate recognition results. Experimental results on segmented character images show that the proposed approach achieves a training accuracy of 93% and a validation accuracy of up to 100% with stable loss values, indicating effective learning under controlled conditions. Although performance decreases when applied to full manuscript images due to segmentation challenges and low contrast, the proposed method demonstrates strong potential for digital preservation of Incung manuscripts. While this study does not directly implement smart city services, the resulting digital manuscript data can support local wisdom–based smart city initiatives by enabling digital cultural documentation and access.
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