Datasets:
Tasks:
Image-to-Text
Sub-tasks:
image-captioning
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
image
image 1
1.7k
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RM 29.70 |
|
- ARTLINE 70 |
|
THANK YOU AND PLEASE COME AGAIN. |
|
MOBILE NO.: |
|
560272 |
|
28.58 |
|
INVOICE NO.: |
|
5.66 |
|
AMOUNT |
|
OCEAN LC PACKAGING ENTERPRISE |
|
S/O NO |
|
DISC |
|
CASH |
|
30/04/2017 (SUN) 20:22 |
|
CASH |
|
PRICE |
|
RM10.14 |
|
ITEM |
|
COMPANY REG NO.: 28982V |
|
DESC |
|
GREEN BEAN |
|
CASH : |
|
TEO HENG STATIONERY & BOOKS |
|
6.30 |
|
GOODS SOLD ARE NOT RETURNABLE |
|
CASHIER: |
|
SERVICE CHARGE |
|
AMOUNT RM |
|
RESTAURANT ORDER CHIT NCR |
|
TEL : 03-87686092 |
|
CHILI ' S |
|
0.00 |
|
019-2616 281 MR.NEO |
|
CHANGE DUE : |
|
Z |
|
TOTAL QTY: 4 |
|
TOTAL SALES(EXCLUDING GST) : |
|
GST REG. NO. : 000155453440 |
|
NO OF ITEMS: 10 |
|
SUBTOTAL (QTY 4) |
|
RM 27.00 |
|
SUB-TOTAL (GST) |
|
11.90 |
|
0 |
|
3.00 |
|
52.00 |
|
GST ID : 001661886464 |
|
12.90 |
|
RM 2.15 |
|
U/P |
|
DISCOUNT |
|
5.80 |
|
FAX:03- 55423213 |
|
2,197.00 SR |
|
1 |
|
GST @6%: $0.50 |
|
SR 100100000035- 1 MEAT + 3 VEGE |
|
REQUIRED TO MAKE NECESSARY ADJUSTMENTS TO ITS |
|
ISS |
|
10.57 |
|
0 |
|
(GST ID NO :000473792512) |
|
TAMPOI,81200 JOHOR BAHRU,JOHOR |
|
AMOUNT(RM) |
|
DATE |
|
6.00 |
|
0 |
|
0.50 SR |
|
1,124 |
|
ROAST CHICKEN RICE |
|
3 |
|
CODE |
|
2.13 |
|
TOTAL PAYABLE: |
|
U.PRICE |
|
TIME: |
|
8809069300708 |
|
CASH |
|
MAKASSAR FRESH MARKET SDN BHD |
|
IBRAHIM |
|
2 |
|
CHANGE |
|
RM |
|
1.65 |
|
SONOFAX - EC THERMAL ROLL |
|
RM |
|
9020529 |
|
REF.: |
|
QTY |
|
CARD COVER (SJB-4013) |
|
13.78 |
|
GSTSUMMARY |
|
RM 8.00 |
|
GST SUMMARY |
|
1 |
|
2.20 |
|
TAN WOON YANN |
|
TOTAL |
|
CHANGE |
|
3X10.49 |
End of preview. Expand
in Dataset Viewer.
This dataset we prepared using the Scanned receipts OCR and information extraction(SROIE) dataset. The SROIE dataset contains 973 scanned receipts in English language. Cropping the bounding boxes from each of the receipts to generate this text-recognition dataset resulted in 33626 images for train set and 18704 images for the test set. The text annotations for all the images inside a split are stored in a metadata.jsonl file.
usage:
from dataset import load_dataset
data = load_dataset("priyank-m/SROIE_2019_text_recognition")
source of raw SROIE dataset: https://www.kaggle.com/datasets/urbikn/sroie-datasetv2
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