ENHANCING HANDWRITTEN DIGIT RECOGNITION ACCURACY ON THE MNIST DATASET USING A HYBRID CNN-BILSTM MODEL WITH DATA AUGMENTATION

Abstract

Handwritten digit recognition is a classic challenge in the field of computer vision and machine learning, and continues to be developed to achieve higher accuracy. This study proposes a hybrid method that combines Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) to enhance performance in handwritten digit classification using the MNIST dataset. CNNs are em-ployed to extract spatial features from digit images, while BiLSTMs are used to capture the temporal patterns and sequential context from the extracted features. To address limitations in data variation and improve the model’s generalization capabilities, the study also applies data augmentation techniques based on image transformations such as rota-tion, translation, scaling, and flipping. Experimental results demonstrate that the hybrid CNN-BiLSTM model with data augmentation signifi-cantly improves classification accuracy compared to baseline ap-proaches without augmentation or without BiLSTM. The model achieved the following accuracy on the MNIST test data: CNN Model Accuracy: Before Augmentation: 98.0%. After Augmentation: 98.5%; CNN-BiLSTM Model Accuracy: Before Augmentation: 98.0%. After Augmentation: 98.7%. These results highlight the effectiveness of the hybrid approach in enhancing handwritten digit recognition perfor-mance. This research contributes to the development of more accurate and robust deep learning models for handwritten image processing