Hybrid CNN + LSTM Model for Accurate Hindi Handwritten Digit Recognition

Significant improvements in the field of Hindi handwriting digit recognition have been demonstrated by recent studies. Modern deep learning models, such as CNN-LSTM architectures, have been successfully used to categorize Hindi handwritten numbers with high accuracy. These models boost recognition performance by utilizing the strengths of extended short-term memory networks for sequence modeling and convolutional neural networks for feature extraction. The availability of vast datasets of Hindi handwritten digits has also aided the development of reliable and precise recognition systems. This study examines the effectiveness of a CNN-LSTM model for Hindi handwritten digit recognition. The objective is to ascertain the model’s accuracy and performance in identifying and classifying handwritten digits in the Hindi script. The study seeks to understand the potential of this model in addressing the challenges associated with Hindi digit recognition and to explore its applicability in real-world scenarios. This study employed a quantitative methodology to evaluate the performance of the CNN-LSTM model for Hindi handwritten digit recognition. A large-scale Hindi handwritten digit dataset was collected and used for training and testing the model. The dataset was divided into training and testing subsets, and the model was trained using the training subset. The CNN-LSTM model achieved an impressive accuracy of 99.6% in accurately recognizing and classifying Hindi handwritten digits, outperforming individual CNN and LSTM models. The study findings demonstrate that the CNN-LSTM model outperforms individual CNN and LSTM models, achieving a remarkable accuracy of 99.6% in Hindi handwritten digit recognition, suggesting its potential for real-world applications and the need for further exploration and optimization.