# A Comparative Study of OCR Architectures for Korean License Plate Recognition: CNN–RNN-Based Models and MobileNetV3–Transformer-Based Models

## TL;DR

This study compares different OCR models for Korean license plate recognition, finding that performance depends on dataset and system design.

## Contribution

The paper introduces a systematic comparison of CNN–RNN and Transformer-based OCR models under identical detection conditions for Korean license plates.

## Key findings

- Transformer-based OCR models have higher computational and memory overhead, limiting real-time deployment.
- Sequence decoder effectiveness is highly dataset-dependent and influenced by ROI stability.
- Tracking-induced error accumulation significantly impacts OCR performance in sequential datasets.

## Abstract

This paper presents a systematic comparative study of optical character recognition (OCR) architectures for Korean license plate recognition under identical detection conditions. Although recent automatic license plate recognition (ALPR) systems increasingly adopt Transformer-based decoders, it remains unclear whether performance differences arise primarily from sequence modeling strategies or from backbone feature representations. To address this issue, we employ a unified YOLOv12-based license plate detector and evaluate multiple OCR configurations, including a CNN with an Attention-LSTM decoder and a MobileNetV3 with a Transformer decoder. To ensure a fair comparison, a controlled ablation study is conducted in which the CNN backbone is fixed to ResNet-18 while varying only the sequence decoder. Experiments are performed on both static image datasets and tracking-based sequential datasets, assessing recognition accuracy, error characteristics, and processing speed across GPU and embedded platforms. The results demonstrate that the effectiveness of sequence decoders is highly dataset-dependent and strongly influenced by feature quality and region-of-interest (ROI) stability. Quantitative analysis further shows that tracking-induced error accumulation dominates OCR performance in sequential recognition scenarios. Moreover, Korean license plate–specific error patterns reveal failure modes not captured by generic OCR benchmarks. Finally, experiments on embedded platforms indicate that Transformer-based OCR models introduce significant computational and memory overhead, limiting their suitability for real-time deployment. These findings suggest that robust license plate recognition requires joint consideration of detection, tracking, and recognition rather than isolated optimization of OCR architectures.