Update README.md

README.md

@@ -1,9 +1,46 @@
----
-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.
+🔢 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
+    🎯 One-pixel stroke differences → 33 ↔ 38, 35 ↔ 36, 53 ↔ 58, 39↔89
+    🌀 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
+🎯 Applications
+    🧪 Benchmarking OCR systems
+    🛡 Studying digit recognition robustness
+    🔑 Training models for noisy / CAPTCHA-like digits
+    🚨 Anomaly detection in digit datasets
+⚙️ 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
+👉 This dataset provides a controlled testbed for studying digit misclassification under visually ambiguous conditions.
+📦 How to Use
+    1️⃣ JSONL format (VQA-style for VLM testing) (merged_puzzles.jsonl)
+    Each entry includes:
+    🖼 image → file path to the digit image
+    ❓ question → natural language query
+    ✅ answer → ground truth numbers
+    2️⃣ CSV format (digit confusion localization)
+    The merged_puzzles.csv file provides metadata about anomaly location:
+    🖼 image → file path
+    📌 location → anomaly position (row, col)
+    merged_puzzles.zip file contains all the images. 
+🚀 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
+🧪 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
+    🔎 Reflective inference mode → improves reasoning under ambiguous digit confusions
+    🏆 Benchmark leader → achieves 78.3 avg. score on OpenCompass (best among <40B param open-source models)
+    📌 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.
+
+This dataset is also made available on other trusted public repositories. One can test VLMs capability of finegrain digit identification.