Handwritten character recognition for Tamil and English presents significant challenges due to the unique scripts and variations in individual handwriting. The primary objective of the research is to create a reliable system capable of accurately recognizing handwritten characters in both Tamil and English scripts using a convolutional neural network (CNN) model. Addressing this problem is crucial for improving digitization efforts, facilitating data entry automation, and enhancing user interfaces in multilingual contexts. Existing systems often lack the accuracy and generalizability needed for effective recognition across different scripts, particularly for underrepresented languages like Tamil. This research aims to fill these gaps by leveraging CNN’s capability to learn complex patterns and features from the data, leading to improved recognition performance. The proposed solution employs a CNN architecture trained on a diverse dataset of handwritten Tamil and English characters. Techniques such as data augmentation, transfer learning, and optimization of hyperparameters are utilized to enhance the model’s accuracy and robustness. Following the recognition of characters, natural language processing (NLP) techniques are applied to summarize the recognized text into a concise sentence. Finally, the recognized text is converted into speech using text-to-speech (TTS) technology, providing an audible representation of the recognized content. The results demonstrate a significant improvement in recognition rates, highlighting the potential of CNNs in multilingual handwritten character recognition tasks. This integrated approach enhances the usability and accessibility of digitized handwritten content.