Applying Explanatory Artificial Intelligence Using LIME to Analyze Arabic Online Handwriting Recognition Models

This study applies interpretable artificial intelligence (XAI) techniques in an unprecedented way to improve transparency in online Arabic handwriting recognition models. By using the LIME (Local Model-Independent Explanations) technique to analyze and interpret a convolutional neural network (CNN) model trained on the ADAB dataset. LIME highlights the key features that the model relies on in handwriting recognition. This research demonstrates the effectiveness of the LIME technique in uncovering the factors that the model relies on in an unconventional way and provides detailed explanations of how the decisions are made, which enhances transparency and increases users’ confidence in these models. The results provide useful information for improving the performance of handwriting recognition systems and a beginning for applying XAI techniques on a larger scale, including using more complex datasets and additional interpretation techniques to gain a deeper understanding of how models work with different handwriting styles. This study paves the way for the use of interpretable techniques in the field of Arabic handwriting recognition.