Abstract:Motivation: In India, the Language Kannada is an ancient and official language in Karnataka State. The study of ancient Kannada scripts from stone carvings, leaf, metal, cloth, paper and other sources enhances our knowledge on the traditions and culture practiced in Karnataka. Due to Poor Quality, variability and the contrast, the Kannada ancient scripts become very challenging to extract the information or to recognize the characters. Objectives: To design a suitable Optical Character Recognition (OCR) techni… Show more
“…This method was introduced because of the low accuracy of inscriptions images using OCR. In addition, [9] suggested a k number of clusters (kmeans clustering) for an ancient Kannada text using scaleinvariant Fourier transform (SIFT) and speeded up robust features (SURF). Moreover, in [10], OCR was used to identify ancient Tamil inscriptions on stone using a feature extracted with the SIFT algorithm.…”
Inscriptions play an important role in preserving historical information. As such, conservation of these inscriptions provides valuable insights into the history and cultural heritage of the region. Musnad inscriptions are considered one of the earliest forms of writing from the Arabian Peninsula, preceding the modern Arabic font; however, most Musnad inscriptions remain unread and untranslated, signifying a substantial loss of historical information. In response, this paper represents a significant contribution to the field by proposing a successful approach to interpreting Musnad inscriptions. To do so, a dataset was prepared from the Saudi Arabian Ministry of Culture and subjected to preprocessing for optimal recognition, a step that entailed several experiments to enhance image quality and preparedness for recognition. The dataset was then trained and tested with 29 classes using three different convolutional neural network (CNN) architectures: Visual Geometry Group 16 (VGG16), Residual Network 50 (ResNet50) and MobileNetV2. Thereafter, the performance of each architecture was evaluated based on its accuracy in recognising Musnad inscriptions. The results demonstrate that VGG16 achieved the highest accuracy of 93.81%, followed by ResNet50 at 89.39% and MobileNetV2 at 80.02%.
“…This method was introduced because of the low accuracy of inscriptions images using OCR. In addition, [9] suggested a k number of clusters (kmeans clustering) for an ancient Kannada text using scaleinvariant Fourier transform (SIFT) and speeded up robust features (SURF). Moreover, in [10], OCR was used to identify ancient Tamil inscriptions on stone using a feature extracted with the SIFT algorithm.…”
Inscriptions play an important role in preserving historical information. As such, conservation of these inscriptions provides valuable insights into the history and cultural heritage of the region. Musnad inscriptions are considered one of the earliest forms of writing from the Arabian Peninsula, preceding the modern Arabic font; however, most Musnad inscriptions remain unread and untranslated, signifying a substantial loss of historical information. In response, this paper represents a significant contribution to the field by proposing a successful approach to interpreting Musnad inscriptions. To do so, a dataset was prepared from the Saudi Arabian Ministry of Culture and subjected to preprocessing for optimal recognition, a step that entailed several experiments to enhance image quality and preparedness for recognition. The dataset was then trained and tested with 29 classes using three different convolutional neural network (CNN) architectures: Visual Geometry Group 16 (VGG16), Residual Network 50 (ResNet50) and MobileNetV2. Thereafter, the performance of each architecture was evaluated based on its accuracy in recognising Musnad inscriptions. The results demonstrate that VGG16 achieved the highest accuracy of 93.81%, followed by ResNet50 at 89.39% and MobileNetV2 at 80.02%.
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