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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.
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