  Explainable AI (XAI) is the study on how humans can be able to understand the
cause of a model's prediction. In this work, the problem of interest is Scene
Text Recognition (STR) Explainability, using XAI to understand the cause of an
STR model's prediction. Recent XAI literatures on STR only provide a simple
analysis and do not fully explore other XAI methods. In this study, we
specifically work on data explainability frameworks, called attribution-based
methods, that explain the important parts of an input data in deep learning
models. However, integrating them into STR produces inconsistent and
ineffective explanations, because they only explain the model in the global
context. To solve this problem, we propose a new method, STRExp, to take into
consideration the local explanations, i.e. the individual character prediction
explanations. This is then benchmarked across different attribution-based
methods on different STR datasets and evaluated across different STR models.
