In order to recognize Kannada characters, this work investigates the merging of deep learning and conventional machine learning techniques. The study attempted to improve character recognition tasks’ accuracy and efficiency by merging the best features of both methods, and also assessing several fusion methods and how they affect the recognition system's performance, offering valuable insights into the best ways to combine these complementing approaches. A dataset comprising a wide range of Kannada characters is utilized for training and evaluation purposes. Various methods are implemented to enhance the quality of input data, followed by feature extraction to represent Kannada characters effectively. Additionally, rotating images methods are employed to augment for improving accuracy of classification models. Experimental results demonstrate the effectiveness of the proposed approaches in accurately classifying Kannada alphabets. Convolution neural network model exhibit superior performance compared to traditional machine learning algorithms, achieving high classification accuracy rates. Observing various probabilities that impact the architecture, training parameters, and data augmentation strategies on classification performance is analyzed and discussed comprehensively. Furthermore, the study investigates the generalization capability of the developed models by evaluating their performance on unseen datasets. The experimental findings suggest that the proposed models achieve competitive performance on train test datasets, seeing their deployment in applications. Overall, our research study contributes to the advancement of Kannada alphabet recognition technology by providing insights into effective methodologies and techniques for accurate classification. The developed models offer promising results and lay the foundation for further research in this domain, with potential applications in OCR systems, document analysis, language processing, and other related fields.