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--- |
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license: apache-2.0 |
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task_categories: |
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- image-text-to-text |
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language: |
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- en |
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size_categories: |
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- 10K<n<100K |
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--- |
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📊 DigitConfuse-23k: A Synthetic Dataset of Digit Confusion Patterns |
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...DigitConfuse-23k is a synthetic dataset containing 23,000 images of digit pairs designed to capture visual anomalies and confusion cases commonly encountered in OCR, CAPTCHA recognition, optical illusions and human digit interpretation tasks. |
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...Each image contains two-digit numbers generated using the Humor-Sans font (font_size=32, cell_w=60, cell_h=40). For each confusion category, ~1000 images are included. |
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🔢 Categories of Digit Anomalies |
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🔸 Digit shape confusion (similar glyphs) → 11 ↔ 17, 21 ↔ 27, 71 ↔ 77 |
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🔄 Mirror / rotation confusion → 69 ↔ 96, 68 ↔ 86, 89↔98, 26 ↔ 62 |
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🎯 One-pixel stroke differences → 33 ↔ 38, 35 ↔ 36, 53 ↔ 58, 39↔89 |
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🌀 Closed vs. open loop confusion → 38 ↔ 88, 98 ↔ 99, 18 ↔ 19, 56↔58, 28↔88 |
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➿ Nearly identical when repeated → 88 ↔ 89, 11 ↔ 12, 55 ↔ 56 |
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👀 Human OCR-like errors (CAPTCHA/OCR cases) → 47 ↔ 17, 57 ↔ 37, 12 ↔ 72, 14 ↔ 74 |
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🎯 Applications |
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🧪 Benchmarking OCR systems |
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🛡 Studying digit recognition robustness |
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🔑 Training models for noisy / CAPTCHA-like digits |
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🚨 Anomaly detection in digit datasets |
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⚙️ Technical Details |
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📂 Total images: 23,000 |
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📑 Categories: 23 confusion pairs |
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✍️ Font: Humor-Sans.ttf |
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🔠 Font size: 32 |
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📏 Image cell size: 60 × 40 pixels, 2400x1000 image resolution |
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👉 This dataset provides a controlled testbed for studying digit misclassification under visually ambiguous conditions. |
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📦 How to Use |
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1️⃣ JSONL format (VQA-style for VLM testing) (merged_puzzles.jsonl) |
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Each entry includes: |
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🖼 image → file path to the digit image |
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❓ question → natural language query |
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✅ answer → ground truth numbers |
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2️⃣ CSV format (digit confusion localization) |
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The merged_puzzles.csv file provides metadata about anomaly location: |
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🖼 image → file path |
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📌 location → anomaly position (row, col) |
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merged_puzzles.zip file contains all the images. |
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🚀 Suggested Use Cases |
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🤖 VLM evaluation → Test Qwen-VL, InternVL, LLaVA on fine-grained OCR tasks |
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📊 OCR benchmarking → Compare CNN-based OCR vs. multimodal LLMs |
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🔄 Data augmentation research → Train models to handle ambiguity |
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🕵️ Anomaly detection → Use confusion pairs as “hard negatives” for OCR |
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🧪 Real-World Testing with Ovis 2.5-9B (Latest Release) |
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I evaluated a subset of images using Ovis 2.5-9B (released Aug 2025). |
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🖼 Native-resolution ViT (NaViT) → preserves fine details for loop/ stroke differences |
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🔎 Reflective inference mode → improves reasoning under ambiguous digit confusions |
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🏆 Benchmark leader → achieves 78.3 avg. score on OpenCompass (best among <40B param open-source models) |
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📌 Observation: Ovis 2.5-9B performed robustly across one-pixel stroke, mirror/rotation, and loop closure confusions, proving this dataset’s value for fine-grained OCR evaluation with VLMs. |
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This dataset is also made available on other trusted public repositories. One can test VLMs capability of finegrain digit identification. |
