Update README.md

README.md

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----
-dataset_info:
-  features:
-  - name: image
-    dtype: image
-  - name: question
-    dtype: string
-  - name: answer
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 2770271247
-    num_examples: 12080
-  download_size: 2118805801
-  dataset_size: 2770271247
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
----
+<h1 align="center">📚 TextBookCover-VQA</h1>
+
+<p align="center">
+  <b>A Real-World Visual Question Answering Dataset for Textbook Cover Understanding</b>
+</p>
+
+<p align="center">
+  📸 1,510 Images &nbsp;•&nbsp;
+  ❓ 12,080 QA Pairs &nbsp;•&nbsp;
+  📱 Mobile-Camera Captured &nbsp;•&nbsp;
+  🧠 VLM Benchmark
+</p>
+
+### 🔍 A Real-World Visual Question Answering Dataset for Textbook Cover Understanding                                                # 📚 TextBookCover-VQA
+🔍 A Real-World Visual Question Answering Dataset for Textbook Cover Understanding
+📸 1,510 real-world textbook cover images
+❓ 12,080 question–answer pairs
+📱 Mobile-camera captured images
+🧠 VLM Evaluation for visual, textual, and semantic understanding
+🔤 Text & Metadata Understanding — title, authors, publisher, subject, etc.
+📐 Spatial Understanding — location and arrangement of cover-page information
+🎨 Visual Understanding — illustrations, graphics, and cover elements  
+📝 Caption
+TextBookCover-VQA is a real-world Visual Question Answering (VQA) dataset designed to evaluate the ability of Vision-Language Models (VLMs) to understand and interpret textbook cover pages captured using mobile cameras. The dataset contains 1,510 textbook cover images and 12,080 question–answer pairs, covering textual, semantic, spatial, and visual attributes of textbook covers. It provides a challenging benchmark for assessing VLM capabilities in text recognition, semantic understanding, spatial reasoning, and visual interpretation of educational book covers under realistic imaging conditions.