# HalalBench: A Multilingual OCR Benchmark for Food Packaging Ingredient Extraction

arXiv: https://arxiv.org/abs/2604.22754 · HTML: https://mlsanity.com/paper/2604.22754/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 25 references that are in the map.
- First posted on arXiv 19 Feb 2026.

## Builds on (most cited first, 5 of 5)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [PP-OCR: A Practical Ultra Lightweight OCR System](https://mlsanity.com/paper/2009.09941/) (2020)
- [Evaluating OCR performance on food packaging labels in South Africa](https://mlsanity.com/paper/2510.03570/) (2025)
- [A Blockchain and Artificial Intelligence based System for Halal Food Traceability](https://mlsanity.com/paper/2410.07305/) (2024)
- [Halal or Not: Knowledge Graph Completion for Predicting Cultural Appropriateness of Daily Products](https://mlsanity.com/paper/2501.05768/) (2025)

## Abstract (arXiv)

No standardized benchmark exists for evaluating OCR on food packaging, despite its critical role in automated halal food verification. Existing benchmarks target documents or scene text, missing the unique challenges of ingredient labels: curved surfaces, dense multilingual text, and sub-8pt fonts. We present HalalBench, the first open multilingual benchmark for food packaging OCR, comprising 1,043 images (50 real, 993 synthetic) with 36,438 annotations in COCO format spanning 14 languages. We evaluate four engines: docTR achieves F1=0.193, ML Kit 0.180, EasyOCR 0.167, while all fail on Japanese (F1=0.000). A clustering ablation shows 36% F1 improvement from our post-processing algorithm. We validate findings through HalalLens (https://halallens.no), a production halal scanner serving 20+ countries. Dataset and code are released under open licenses.