Image quality determination of palm leaf heritage documents using integrated discrete cosine transform features with vision transformer

Classification of Palm leaf images into various quality categories is an important step towards the digitization of these heritage documents. Manual inspection and categorization is not only laborious, time-consuming and costly but also subject to inspector’s biases and errors. This study aims to automate the classification of palm leaf document images into three different visual quality categories. A comparative analysis between various structural and statistical features and classifiers against deep neural networks is performed. VGG16, VGG19 and ResNet152v2 architectures along with a custom CNN model are used, while Discrete Cosine Transform (DCT), Grey Level Co-occurrence Matrix (GLCM), Tamura, and Histogram of Gradient (HOG) are chosen from the traditional methods. Based on these extracted features, various classifiers, namely, k-Nearest Neighbors (k-NN), multi-layer perceptron (MLP), Support Vector Machines (SVM), Decision Tree (DT) and Logistic Regression (LR) are trained and evaluated. Accuracy, precision, recall, and F1 scores are used as performance metrics for the evaluation of various algorithms. Results demonstrate that CNN embeddings and DCT features have emerged as superior features. Based on these findings, we integrated DCT with a Vision Transformer (ViT) for the document classification task. The result illustrates that this incorporation of DCT with ViT outperforms all other methods with 96% train F1 score and a test F1 score of 90%.