Upload The ReadMe and Metadata

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README.md

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+# Wild Khmer Grounding Dataset (OCR + Visual Grounding)
+
+This dataset is a high-quality collection of Khmer text images "in the wild." It is a **derivative work** specifically enhanced for **Visual Grounding** and **High-Accuracy OCR** fine-tuning.
+
+## 📌 Provenance & Credits
+
+- **Original Source:** [Khmer Word Dataset (Kaggle)](https://www.kaggle.com/datasets/keosaly/wildkhmerst-dataset)
+- **Original Owner:** **Saly KEO**
+- **Modification:** The original dataset (which provided single text labels per image) has been re-annotated and processed to include **normalized bounding boxes** for multi-region text detection and grounding tasks.
+
+## 📄 License
+
+This dataset is distributed under the **Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)** license.
+
+- **Attribution:** You must give appropriate credit to Saly KEO (original source) and the current maintainer.
+- **Non-Commercial:** You may not use the material for commercial purposes.
+
+---
+
+## Dataset Summary
+
+This version of the dataset provides **localized bounding boxes** for every text region within an image. This enables Vision-Language Models (VLMs) to learn spatial reasoning—understanding exactly _where_ a piece of text is located before reading it.
+
+- **Primary Language:** Khmer (UTF-8)
+- **Format:** Hugging Face Parquet (Embedded Images)
+- **Normalization:** Coordinates scaled to **0-1000**.
+- **Task Type:** Visual Grounding, Document Intelligence, Khmer OCR.
+
+## Dataset Structure
+
+| Column  | Type     | Description                                                                          |
+| :------ | :------- | :----------------------------------------------------------------------------------- |
+| `image` | `Image`  | The source image (embedded bytes).                                                   |
+| `text`  | `String` | A JSON string containing a list of text regions and their normalized bounding boxes. |
+
+### JSON Label Example
+
+```json
+{
+  "regions": [
+    {
+      "bbox_2d": [ymin, xmin, ymax, xmax],
+      "label": "សោភ័ណ ស៊ីថា"
+    }
+  ]
+}
+```
+
+## How to Use
+
+### Loading the Dataset
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("vichetkao/wild_khmer")
+print(dataset['train'][0])
+```
+
+### Formatting for Qwen3-VL / Unsloth
+
+```python
+import json
+
+def convert_to_conversation(sample):
+    grounding_data = json.loads(sample["text"])
+    instruction = "Detect all Khmer text regions in this image and return the labels with their bounding boxes in JSON format."
+
+    return {
+        "messages": [
+            {"role": "user", "content": [{"type": "text", "text": instruction}, {"type": "image", "image": sample["image"]}]},
+            {"role": "assistant", "content": [{"type": "text", "text": sample["text"]}]}
+        ]
+    }
+```
+
+## Supported Models
+
+- **Qwen3-VL-8B / 32B**
+- **Qwen2-VL Series**
+- **InternVL2**
+- **Llama-3.2-Vision**
+
+## Citation
+
+If you use this dataset, please credit the original author:
+
+```text
+Original Creator: Saly KEO (Kaggle)
+Derivative Work: Kao Vichet
+License: CC BY-NC 4.0
+```

annotation_data.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f175028c",
+   "metadata": {},
+   "source": [
+    "## The \"Wild Khmer\" Dataset Creation Script"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "1ed4f71e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded 10000 images from JSON\n",
+      "Processing images and labels... This may take a while.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "42c314cacca648d6a0e8b4df6093a9d9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating train split: 0 examples [00:00, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 10000/10000 [02:28<00:00, 67.53it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2b0fef7ef288405c908d8e5ca92348fe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading dataset shards:   0%|          | 0/17 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving Parquet files...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bd6d7d400c8f46b5a10501516cbec7b3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Creating parquet from Arrow format:   0%|          | 0/92 [00:00<?, ?ba/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "68cb344590af4837bb993bc6e0e1bef0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Creating parquet from Arrow format:   0%|          | 0/11 [00:00<?, ?ba/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DONE ✅\n",
+      "Wild Train file: wild_khmer_train.parquet\n"
+     ]
+    }
+   ],
+   "source": [
+    "import json\n",
+    "import os\n",
+    "import pandas as pd\n",
+    "from PIL import Image\n",
+    "from tqdm import tqdm\n",
+    "from datasets import Dataset, Features, Image as datasets_Image, Value\n",
+    "\n",
+    "# -------------------------\n",
+    "# CONFIG\n",
+    "# -------------------------\n",
+    "JSON_PATH = \"info.json\" # Your CADT JSON file\n",
+    "IMAGE_FOLDER = \"images/images\"              # Folder with your JPGs\n",
+    "TRAIN_OUT = \"wild_khmer_train.parquet\"\n",
+    "TEST_OUT = \"wild_khmer_test.parquet\"\n",
+    "TEST_SPLIT = 0.1\n",
+    "\n",
+    "# -------------------------\n",
+    "# LOAD DATA\n",
+    "# -------------------------\n",
+    "with open(JSON_PATH, 'r', encoding='utf-8') as f:\n",
+    "    via_data = json.load(f)\n",
+    "print(f\"Loaded {len(via_data)} images from JSON\")\n",
+    "\n",
+    "# -------------------------\n",
+    "# DATA GENERATOR\n",
+    "# -------------------------\n",
+    "def generate_examples():\n",
+    "    for key, data in tqdm(via_data.items()):\n",
+    "        filename = data['filename']\n",
+    "        image_path = os.path.join(IMAGE_FOLDER, filename)\n",
+    "\n",
+    "        if not os.path.exists(image_path):\n",
+    "            continue\n",
+    "\n",
+    "        # 1. Get Image Dimensions for Normalization\n",
+    "        try:\n",
+    "            with Image.open(image_path) as img:\n",
+    "                width, height = img.size\n",
+    "                # Read raw bytes for embedding\n",
+    "                with open(image_path, \"rb\") as f:\n",
+    "                    img_bytes = f.read()\n",
+    "        except Exception as e:\n",
+    "            print(f\"Error loading {filename}: {e}\")\n",
+    "            continue\n",
+    "\n",
+    "        # 2. Process Regions (Polygons -> Normalized Bounding Boxes)\n",
+    "        regions_list = []\n",
+    "        for region in data['regions']:\n",
+    "            try:\n",
+    "                # Extract coordinates\n",
+    "                xs = region['shape_attributes']['all_points_x']\n",
+    "                ys = region['shape_attributes']['all_points_y']\n",
+    "                label = region['region_attributes']['label']\n",
+    "\n",
+    "                # Convert Polygon to Bounding Box (ymin, xmin, ymax, xmax)\n",
+    "                xmin, xmax = min(xs), max(xs)\n",
+    "                ymin, ymax = min(ys), max(ys)\n",
+    "\n",
+    "                # Normalize to 0-1000 scale (Qwen3-VL Standard)\n",
+    "                n_xmin = int((xmin / width) * 1000)\n",
+    "                n_xmax = int((xmax / width) * 1000)\n",
+    "                n_ymin = int((ymin / height) * 1000)\n",
+    "                n_ymax = int((ymax / height) * 1000)\n",
+    "\n",
+    "                # Format as a dictionary for the grounding task\n",
+    "                regions_list.append({\n",
+    "                    \"bbox_2d\": [n_ymin, n_xmin, n_ymax, n_xmax],\n",
+    "                    \"label\": label\n",
+    "                })\n",
+    "            except KeyError:\n",
+    "                continue # Skip regions without labels or points\n",
+    "\n",
+    "        # 3. Create the final text label (JSON string)\n",
+    "        # This will be processed by your convert_to_conversation function\n",
+    "        grounding_json = json.dumps({\"regions\": regions_list}, ensure_ascii=False)\n",
+    "\n",
+    "        yield {\n",
+    "            \"image\": img_bytes,\n",
+    "            \"text\": grounding_json\n",
+    "        }\n",
+    "\n",
+    "# -------------------------\n",
+    "# DATASET FEATURES\n",
+    "# -------------------------\n",
+    "features = Features({\n",
+    "    \"image\": datasets_Image(),  # embedded image bytes\n",
+    "    \"text\": Value(\"string\"),    # JSON string of boxes and text\n",
+    "})\n",
+    "\n",
+    "# -------------------------\n",
+    "# CREATE & SAVE\n",
+    "# -------------------------\n",
+    "print(\"Processing images and labels... This may take a while.\")\n",
+    "ds = Dataset.from_generator(generate_examples, features=features)\n",
+    "\n",
+    "# Shuffle and split\n",
+    "ds = ds.train_test_split(test_size=TEST_SPLIT)\n",
+    "\n",
+    "print(\"Saving Parquet files...\")\n",
+    "ds[\"train\"].to_parquet(TRAIN_OUT)\n",
+    "ds[\"test\"].to_parquet(TEST_OUT)\n",
+    "\n",
+    "print(\"DONE ✅\")\n",
+    "print(f\"Wild Train file: {TRAIN_OUT}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1f80720",
+   "metadata": {},
+   "source": [
+    "## Open The Image Dataset For Checking"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "57395385",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d2da1e3e954a45fdb05b47c8d38e66b1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading dataset shards:   0%|          | 0/17 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'PIL.JpegImagePlugin.JpegImageFile'>\n",
+      "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=960x1280 at 0x219E9F9BEC0>\n"
+     ]
+    }
+   ],
+   "source": [
+    "from datasets import Dataset\n",
+    "ds = Dataset.from_parquet(\"wild_khmer_train.parquet\")\n",
+    "sample = ds[1][\"image\"]\n",
+    "print(type(sample))\n",
+    "print(sample)\n",
+    "sample.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2307a106",
+   "metadata": {},
+   "source": [
+    "## DUMMY DATASET ROW"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa809986",
+   "metadata": {},
+   "source": [
+    "```python\n",
+    "{\n",
+    " 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1200x800>,\n",
+    " 'text': '{\"regions\": [{\"bbox_2d\": [150, 200, 300, 800], \"label\": \"អាហារដ្ឋានមិត្តភាព\"}, {\"bbox_2d\": [850, 400, 920, 600], \"label\": \"012 345 678\"}]}'\n",
+    "}\n",
+    "```"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

info.json

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+size 19973399