Automatic English Grammar Correction Tool using Recurrent Neural Network

This paper dives deep into a deep learning approach for automatic English grammar error correction, leveraging the power of Recurrent Neural Networks (RNNs). RNNs excel at processing sequential data like language, making them adept at analyzing sentence structure and pinpointing grammatical errors. The research proposes a system design featuring an RNN architecture. This architecture ingests text, detects errors, and suggests appropriate corrections. The network is meticulously trained on a massive dataset meticulously crafted to include both grammatically correct and incorrect sentences. Through this training, the RNN learns the intricate relationships between words, enabling it to identify and rectify a wide range of grammatical mistakes. To assess the system’s efficacy, the research evaluates its performance on unseen data. This evaluation demonstrates the system’s effectiveness and its potential to significantly improve communication and language learning by providing real-time feedback and enhancing writing accuracy of $\mathbf{9 8 \%}$ compared to other existing methods.