Bi-directional GRU-Based Approach for Multi-Class Text Error Identification System

In today's highly competitive academic and research landscape, the impression authors leave on readers is paramount to their reputation and success. Spelling errors can significantly undermine an author's credibility and negatively impact their standing within the scholarly community. To mitigate this risk, it is imperative that authors thoroughly proofread their content before submitting it to prestigious conference journals or sharing it with fellow researchers. This paper introduces a novel solution to streamline the error-checking process and empower authors to focus on other critical aspects of their projects. We propose a sophisticated text classification model, leveraging the vast capabilities of the Recurrent Neural Network (RNN) architecture, which was trained on the extensive C4 dataset sourced from the TensorFlow Library. This model exhibits exceptional proficiency in rectifying spelling mistakes across a wide range of contexts, thereby bolstering the overall quality and clarity of written work. Notably, our text classification model trained with Bi-Directional GRU method showcases a remarkable accuracy of 67% in classifying errors into four distinct categories, providing insightful feedback to authors for further improvement.