Create README.md

README.md

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+---
+task_categories:
+- token-classification
+- image-to-text
+- object-detection
+tags:
+- invoices
+- finance
+- document-understanding
+- layoutlm
+- ner
+features:
+  id:
+    dtype: int32
+  words:
+    dtype: string
+    sequence: string
+  boxes:
+    dtype: int64
+    sequence:
+      sequence: int64
+  ner_tags:
+    class_label:
+      names:
+        0: O
+        1: B-BALANCE_DUE
+        2: B-BANK_NAME
+        3: B-CLIENT_ADDRESS
+        4: B-CLIENT_EMAIL
+        5: B-CLIENT_NAME
+        6: B-CLIENT_PHONE
+        7: B-CLIENT_TAX_ID
+        8: B-CURRENCY
+        9: B-DUE_DATE
+        10: B-IBAN
+        11: B-INVOICE_DATE
+        12: B-INVOICE_NUMBER
+        13: B-ITEM_DESC
+        14: B-ITEM_NO
+        15: B-LINE_DISCOUNT
+        16: B-LINE_GROSS_WORTH
+        17: B-LINE_NET_WORTH
+        18: B-LINE_UNIT_PRICE
+        19: B-LINE_VAT
+        20: B-ORDER_NUMBER
+        21: B-QTY
+        22: B-REFERENCE_NUMBER
+        23: B-SELLER_ADDRESS
+        24: B-SELLER_EMAIL
+        25: B-SELLER_NAME
+        26: B-SELLER_PHONE
+        27: B-SELLER_TAX_ID
+        28: B-SUBTOTAL
+        29: B-TOTAL_GROSS_WORTH
+        30: B-TOTAL_NET_WORTH
+        31: B-TOTAL_VAT
+        32: I-BANK_NAME
+        33: I-CLIENT_ADDRESS
+        34: I-CLIENT_EMAIL
+        35: I-CLIENT_NAME
+        36: I-CLIENT_PHONE
+        37: I-INVOICE_NUMBER
+        38: I-ITEM_DESC
+        39: I-LINE_NET_WORTH
+        40: I-LINE_UNIT_PRICE
+        41: I-SELLER_ADDRESS
+        42: I-SELLER_EMAIL
+        43: I-SELLER_NAME
+        44: I-SELLER_PHONE
+        45: I-SELLER_TAX_ID
+        46: I-TOTAL_NET_WORTH
+  image:
+    dtype: image
+---
+
+# Merged Invoice NER Dataset
+
+## Dataset Description
+
+This dataset consists of processed invoice documents intended for **Document Layout Analysis** and **Named Entity Recognition (NER)** tasks (e.g., training LayoutLM, LayoutLMv2, LayoutLMv3, or LiLT).
+
+The data was created by merging multiple invoice datasets. It includes the document images, OCR-extracted words, bounding boxes, and NER tags.
+
+### Supported Tasks
+- **Token Classification:** Extracting entities like `INVOICE_NUMBER`, `TOTAL_GROSS_WORTH`, `SELLER_NAME`, etc.
+- **Key Information Extraction (KIE)**
+
+## Data Structure
+
+Each example in the dataset contains the following fields:
+
+- **`id`**: A unique identifier for the document.
+- **`image`**: The PIL Image of the invoice.
+- **`words`**: List of strings (tokens) obtained via OCR.
+- **`boxes`**: List of bounding boxes corresponding to the words.
+  - **Format:** `[x_min, y_min, x_max, y_max]`
+  - **Note:** These boxes are likely in **absolute pixel coordinates** (based on the image size) and **NOT normalized** to 0-1000. You must normalize them before inputting them into models like LayoutLM.
+- **`ner_tags`**: List of class IDs (integers) in BIO (Beginning, Inside, Outside) format.
+
+## Important Usage Warnings
+
+### 1. Bounding Box Normalization Required
+The `boxes` in this dataset are likely stored in absolute pixel values (e.g., `0-2500`). Models like LayoutLM expect boxes normalized to a `0-1000` scale.
+
+**How to handle this:**
+When loading the dataset, you should normalize the boxes relative to the image size:
+
+```python
+def normalize_box(box, width, height):
+    return [
+        int(1000 * (box[0] / width)),
+        int(1000 * (box[1] / height)),
+        int(1000 * (box[2] / width)),
+        int(1000 * (box[3] / height)),
+    ]
+
+def preprocess(example):
+    w, h = example['image'].size
+    # Normalize boxes to 0-1000
+    example['boxes'] = [normalize_box(box, w, h) for box in example['boxes']]
+    return example
+
+dataset = load_dataset("your-username/dataset-name")
+dataset = dataset.map(preprocess)
+
+2. Mixed OCR Sources
+This dataset was created by merging sources that may have used different OCR engines (e.g., Tesseract, EasyOCR, or commercial APIs).
+Some documents may have slightly different bounding box tightness or word-splitting logic.
+Ensure your training pipeline is robust to slight variations in OCR quality.
+3. Missing I-Tags for some entities
+The label map includes B- (Beginning) tags for all entities, but some entities (like BALANCE_DUE, TOTAL_VAT, IBAN) do not have a corresponding I- (Inside) tag in the schema.
+If a value spans multiple words (e.g., "Total VAT"), the model may only be able to label the first word, or you may need to map the subsequent words to O or a generic tag during training.
+4. Privacy / PII
+This dataset contains invoice data. While processed, users should handle the data with care regarding Personally Identifiable Information (PII) such as names, addresses, and financial details.