  The most prevalent scope of interest for OCR applications used to be scanned
documents, but it has now shifted towards the natural scene. Despite the change
of times, the existing evaluation methods are still based on the old criteria
suited better for the past interests. In this paper, we propose PopEval, a
novel evaluation approach for the recent OCR interests. The new and past
evaluation algorithms were compared through the results on various datasets and
OCR models. Compared to the other evaluation methods, the proposed evaluation
algorithm was closer to the human's qualitative evaluation than other existing
methods. Although the evaluation algorithm was devised as a character-level
approach, the comparative experiment revealed that PopEval is also compatible
on existing benchmark datasets annotated at word-level. The proposed evaluation
algorithm is not only applicable to current end-to-end tasks, but also suggests
a new direction to redesign the evaluation concept for further OCR researches.
