The "Sanskrit Character Dataset" includes 44 classes of handwritten Sanskrit characters, designed to support research in optical character recognition (OCR) and machine learning for ancient languages. Each class represents a unique Sanskrit letter, collected in various handwriting styles to ensure diversity and robustness. For each class, 50 to 80 images are included. To ensure diversity and real-world applicability, the letters were written in various handwriting styles.

The potato plant disease detection dataset comprises 5,748 images of potato leaves categorized into six classes: Early Blight (1,000), Fungi (748), Healthy (1,000), Late Blight (1,000), Pest (1,000), and Virus (1,000). The dataset was collected from various open-access sources and integrated class-wise for comprehensive analysis. This dataset provides a robust foundation for training and evaluating convolutional neural networks in plant disease detection.

This dataset presents the recognition of handwritten hieroglyphic alphabets. In this dataset we use consisting of 18 distinct classes of hieroglyphs alphabets. The dataset is designed to facilitate research in the field of ancient script recognition, particularly focusing on handwriting variability and pattern recognition. Each class represents a unique hieroglyph, with samples collected to ensure a diverse range of writing styles. To create this dataset, 25 students each handwrote samples for all 18 classes of hieroglyphs. Afterward, we carefully photographed each image.