This paper presents the development of a deep learning model for recognizing handwritten Kannada characters. Kannada character recognition presents unique challenges due to the complexity of the script and the variety of symbols. To address these, we utilize a hybrid model combining ResNet50 and VGG16 architectures. ResNet50 is leveraged for its ability to train deep networks on complex patterns, while VGG16 excels in capturing detailed feature representations. The model is trained on carefully preprocessed datasets, optimized through iterative parameter tuning to ensure high accuracy and robustness. The backend infrastructure uses Flask and TensorFlow, with the frontend built using JavaScript, HTML, and CSS. The system features a sketchpad where users can draw Kannada characters, which are then processed by the deep learning model for recognition. An interactive tool further supports language learning. Through extensive testing, the system has proven to be reliable and effective. This project represents a significant advancement in automated Kannada language processing, offering a powerful tool for character recognition. By enabling accurate, efficient recognition, it contributes to promoting linguistic diversity and inclusivity, making it an invaluable resource for Kannada language processing applications.