Update README.md

README.md

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+---
+license: apache-2.0
+task_categories:
+- image-text-to-text
+language:
+- en
+size_categories:
+- 10K<n<100K
+---
 📊 DigitConfuse-23k: A Synthetic Dataset of Digit Confusion Patterns
     ...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. 
     ...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
     🔸 Digit shape confusion (similar glyphs) → 11 ↔ 17, 21 ↔ 27, 71 ↔ 77
     🔄 Mirror / rotation confusion → 69 ↔ 96, 68 ↔ 86, 89↔98,  26 ↔ 62
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     🌀 Closed vs. open loop confusion → 38 ↔ 88, 98 ↔ 99, 18 ↔ 19, 56↔58, 28↔88
     ➿ Nearly identical when repeated → 88 ↔ 89, 11 ↔ 12, 55 ↔ 56
     👀 Human OCR-like errors (CAPTCHA/OCR cases) → 47 ↔ 17, 57 ↔ 37, 12 ↔ 72, 14 ↔ 74
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 🎯 Applications
     🧪 Benchmarking OCR systems
     🛡 Studying digit recognition robustness
     🔑 Training models for noisy / CAPTCHA-like digits
     🚨 Anomaly detection in digit datasets
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 ⚙️ Technical Details
 📂 Total images: 23,000
     📑 Categories: 23 confusion pairs
     ✍️ Font: Humor-Sans.ttf
     🔠 Font size: 32
     📏 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
     1️⃣ JSONL format (VQA-style for VLM testing) (merged_puzzles.jsonl)
     Each entry includes:
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     🖼 image → file path
     📌 location → anomaly position (row, col)
     merged_puzzles.zip file contains all the images. 
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 🚀 Suggested Use Cases
     🤖 VLM evaluation → Test Qwen-VL, InternVL, LLaVA on fine-grained OCR tasks
     📊 OCR benchmarking → Compare CNN-based OCR vs. multimodal LLMs
     🔄 Data augmentation research → Train models to handle ambiguity
     🕵️ Anomaly detection → Use confusion pairs as “hard negatives” for OCR
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 🧪 Real-World Testing with Ovis 2.5-9B (Latest Release)
     I evaluated a subset of images using Ovis 2.5-9B (released Aug 2025).
     🖼 Native-resolution ViT (NaViT) → preserves fine details for loop/ stroke differences