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7. Limitations and pitfalls

• Domain gap: models trained on MIDV-260 may not generalize to different document types, capture devices, or regions.
• Bias: dataset composition may underrepresent certain ID formats or languages.
• Quality: real-world OCR performance may degrade under severe blur, glare, or occlusion.

2. Dataset summary

• MIDV-260 is a benchmark dataset of 260 identity-document types (images captured under varied conditions) used for document detection, localization, OCR, and recognition research.
• It contains multiple capture scenarios (controlled lighting, cluttered background, different viewpoints) and ground-truth annotations for experiments.

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Performance: Critics and viewers generally praise Yamate’s acting. She is adept at conveying the specific "embarrassment" required by the script, which adds a layer of psychological realism to the scenes.