Abstract
This study evaluates four open-source Optical Character Recognition (OCR) systems which are Tesseract, EasyOCR, PaddleOCR, and TrOCR on real world food packaging images. The aim is to assess their ability to extract ingredient lists and nutrition facts panels. Accurate OCR for packaging is important for compliance and nutrition monitoring but is challenging due to multilingual text, dense layouts, varied fonts, glare, and curved surfaces. A dataset of 231 products (1,628 images) was processed by all four models to assess speed and coverage, and a ground truth subset of 113 images (60 products) was created for accuracy evaluation. Metrics include Character Error Rate (CER), Word Error Rate (WER), BLEU, ROUGE-L, F1, coverage, and execution time. On the ground truth subset, Tesseract achieved the lowest CER (0.912) and the highest BLEU (0.245). EasyOCR provided a good balance between accuracy and multilingual support. PaddleOCR achieved near complete coverage but was slower because it ran on CPU only due to GPU incompatibility, and TrOCR produced the weakest results despite GPU acceleration. These results provide a packaging-specific benchmark, establish a baseline, and highlight directions for layout-aware methods and text localization.
Abstract (translated)
这项研究评估了四种开源光学字符识别（OCR）系统——Tesseract、EasyOCR、PaddleOCR和TrOCR在真实世界食品包装图像上的表现。该研究的目的是评估这些系统提取成分列表和营养信息面板的能力。对于包装上的准确OCR来说，合规性和营养监测非常重要，但由于多语言文本、密集布局、不同字体、反光以及曲面等因素的存在，这一任务具有挑战性。 为了评估速度和覆盖率，所有四个模型都处理了一个包含231种产品（共1,628张图像）的数据集，并为准确性评估创建了113张图像（来自60种产品）的基准数据。评估指标包括字符错误率（CER）、单词错误率（WER）、BLEU、ROUGE-L、F1分数、覆盖率和执行时间。 在基准数据子集中，Tesseract实现了最低的字符错误率（CER为0.912）和最高的BLEU得分（0.245）。EasyOCR在准确性和多语言支持之间提供了良好的平衡。PaddleOCR达到了几乎完全的覆盖范围，但由于与GPU不兼容而仅运行于CPU上，因此速度较慢。尽管有GPU加速，但TrOCR的表现最弱。 这些结果为包装特定领域的基准测试奠定了基础，并突显了布局感知方法和文本定位方向的重要性。
URL
https://arxiv.org/abs/2510.03570
