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• JaWildText is a new benchmark containing 3,241 instances from 2,961 images with 1.12 million annotated characters spanning 3,643 unique character types for evaluating Japanese scene text understanding.method 95%
• The best evaluated vision-language model achieves an average score of only 0.64 across the three tasks in JaWildText, indicating robust Japanese text understanding in the wild remains unsolved.result 90%
• Error analysis shows recognition remains the dominant bottleneck for VLMs on Japanese scene text, with kanji posing a particular challenge.result 90%
• In the Receipt KIE task, format-constrained fields like 'time' are relatively well handled (accuracy above 0.90 for top models), while 'store_name' and 'store_address' remain difficult with best accuracies of only 0.16 and 0.55 respectively.result 85%
• Qwen3-VL-8B outperforms InternVL3.5-38B by 0.09 Overall score, indicating architecture and training data composition can matter more than parameter count alone.result 80%
• The best OCR-specialized model (olmOCR-2-7B, score 0.74) falls below the best general-purpose VLM (Qwen3-VL-8B, score 0.79) on Handwriting OCR at comparable parameter scales.result 85%
• Existing Japanese visual text datasets have primarily focused on scanned documents, leaving in-the-wild scene text underexplored.limitation 85%
• Multilingual benchmarks that include Japanese often fail to capture language-specific complexities like mixed scripts, frequent vertical writing, and large character inventory.limitation 80%

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