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Updated: Jan 13, 2026

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A Robust Framework for Coffee Bean Package Label Recognition: Integrating Image Enhancement with Vision-Language OCR

Thi-Thu-Huong Le1, Yeonjeong Hwang2, Ahmada Yusril Kadiptya2

  • 1Blockchain Platform Research Center, Pusan National University, Busan 609735, Republic of Korea.

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Summary
This summary is machine-generated.

This study introduces an improved Optical Character Recognition (OCR) pipeline for coffee bean package labels, enhancing product tracking and brand verification. The new method significantly boosts text recognition accuracy across challenging real-world conditions.

Keywords:
OCRQwen-VLcoffee bean packageimage enhancementimage-to-textinstruction LLMlabel detectionvision large model

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Area of Science:

  • Computer Vision
  • Natural Language Processing
  • Machine Learning

Background:

  • Text recognition on coffee bean packaging is crucial for product tracking and brand verification.
  • Challenges include variable image quality, diverse packaging, and environmental factors.

Purpose of the Study:

  • To develop an advanced Optical Character Recognition (OCR) pipeline for coffee bean package labels.
  • To improve text recognition accuracy and reliability in real-world scenarios.

Main Methods:

  • A pipeline combining image enhancement techniques and a Vision-Language (VL) Qwen VL-based OCR model with structured prompts.
  • Development and evaluation of four Qwen-VL OCR variants using prompt engineering.
  • Creation of a coffee bean package image dataset (LRCB and HRCB) with diverse real-world challenges.
  • Comparison against baseline OCR models: DocTR, PaddleOCR, EasyOCR, and Tesseract.

Main Results:

  • The proposed Qwen-VL OCR variants demonstrated significant improvements over baseline methods.
  • Evaluation metrics included Levenshtein distance, Cosine similarity, Jaccard index, Exact Match, BLEU, and ROUGE scores.
  • The framework showed strong generalization capabilities on the public POIE dataset.

Conclusions:

  • The developed pipeline offers a practical and reliable solution for coffee bean package label recognition.
  • The approach effectively addresses challenges posed by image quality and environmental variations.
  • This work advances the state-of-the-art in specialized OCR applications.