Extract transaction infomation from scaned pdf. I used statement document statement document of Thuy Tien for example. The source code not included OCR model, instead, I used google OCR services to have best performance. Feel free to become Contributors!
- Google cloud api | Cloud Vision API
- Tài liệu công khai SAO KÊ MIỀN TRUNG
git clone https://github.com/hungtooc/transaction_ocr.git
pip install -r requirements.txt
- Download raw pdf files from Drive link: https://drive.google.com/drive/folders/1SoWOGaAy92tZUgG7mwhJzoeBsDpxVO80?usp=sharing
- Extract & put it in data/input
PDF password: Vcbsaoke@2021
python tools/pdf-to-images.py --pdf-password Vcbsaoke@2021
usage: pdf-to-images.py [-h] [--pdf-dir PDF_DIR] [--output-dir OUTPUT_DIR] [--pdf-password PDF_PASSWORD] [--from-page-no FROM_PAGE_NO] [--to-page-no TO_PAGE_NO] [--fix-page-number FIX_PAGE_NUMBER]
optional arguments:
-h, --help show this help message and exit
--pdf-dir PDF_DIR dir to pdf files
--output-dir OUTPUT_DIR
dir to save images
--pdf-password PDF_PASSWORD
pdf password
--from-page-no FROM_PAGE_NO
extra image from page
--to-page-no TO_PAGE_NO
extra image to page
--fix-page-number FIX_PAGE_NUMBER
fix page number (page_no += fix_page_number)
The source perform the basic steps to extract transaction information, you may want to add additional processing to optimize the source code in lines marked #todo.
python run.py
usage: run.py [-h] [--image-dir IMAGE_DIR] [--output-respone-dir OUTPUT_RESPONE_DIR] [--output-content-dir OUTPUT_CONTENT_DIR] [--processed-log-file PROCESSED_LOG_FILE]
optional arguments:
-h, --help show this help message and exit
--image-dir IMAGE_DIR
dir to images
--output-respone-dir OUTPUT_RESPONE_DIR
dir to save api respone
--output-content-dir OUTPUT_CONTENT_DIR
dir to save transaction content
--processed-log-file PROCESSED_LOG_FILE
path to log file
- Step 1. Find header & footer.
- Step 2. Re-rotate image based on header-corner.
- Step 3. Clean image.
- Step 4. Call request google-ocr api. (include:text-detection & text-recognition
- Step 5. Detect transaction line.
- Step 6. Classify transaction content each line & each content type.
- Step 7. Save transactions content to csv.
| TNX Date | Doc No | Debit | Credit | Balance | Transaction in detail | (note) |
|---|---|---|---|---|---|---|
| 13/10/2020 | 5091.55821 | 100.000 | 586062.131020.075756.Ung ho mien trung FT20287151644070 | page_1 | ||
| 13/10/2020 | 5091.56080 | 1.000.000 | 586279.131020.075829.Ung ho dong bao mien Trung FT20287592192480 | page_1 | ||
| 13/10/2020 | 5091.56138 | 200.000 | 219987.131020.075839.Trinh Thi Thu Thuy chuyen tien ung ho mien Trung | page_1 | ||
| 13/10/2020 | 5091.56155 | 100.000 | 586295.131020.075826.UH mien trung FT20287432289640 | page_1 | ||
| 13/10/2020 | 5078.68388 | 500.000 | MBVCB.807033343.PHAM THUY TRANG chuyen tien ung ho tu thien.CT tu 0561000606153 PHAM THUY TRANG toi 0181003469746 TRAN THI THUY TIEN | page_1 | ||
| 13/10/2020 | 5091.56261 | 1.000.000 | 184997.131020.075853.Em gui giup do ba con vung lu | page_1 | ||
| 13/10/2020 | 5078.68496 | 200.000 | MBVCB.807033583.Ung ho mien trung.CT tu 0051000531310 HUYNH THI NHU Y toi 0181003469746 TRAN THI THUY TIEN | page_1 | ||
| 13/10/2020 | 5078.68526 | 100.000 | MBVCB.807033514.ung ho mien trung.CT tu 0481000903279 NGUYEN THI HUONG AN toi 0181003469746 TRAN THI THUY TIEN | page_1 | ||
| 13/10/2020 | 5091.56381 | 100.000 | 479592.131020.075909.ho tro mien trung | page_1 | ||
| 13/10/2020 | 5078.68537 | 500.000 | MBVCB.807034561.Ung ho Mien trung.CT tu 0721000588146 LE THI HONG DIEM toi 0181003469746 TRAN THI THUY TIEN | page_1 | ||
| 13/10/2020 | 5091.56405 | 200.000 | 292363.131020.075845.Ngan hang TMCP Ngoai Thuong Viet Nam 0181003469746 LUC NGHIEM LE chuyen khoan ung ho mien trung | page_1 | ||
| 13/10/2020 | 5091.56410 | 500.000 | 479627.131020.075913.Ung ho mien trung | page_1 |
Export each csv directory to an excel file. Example:
python tools/export-excel.py --csv-dir "data/content/TÀI KHOẢN XXX746 (Pass_ Vcbsaoke@2021)/TỪ 13.10.20 ĐẾN 23.11.20/1. TRANG 1 -1000.pdf"
usage: export-excel.py [-h] --csv-dir CSV_DIR [--output-dir OUTPUT_DIR] [--transaction-template TRANSACTION_TEMPLATE] [--filename FILENAME]
optional arguments:
-h, --help show this help message and exit
--csv-dir CSV_DIR csv dir
--output-dir OUTPUT_DIR
output dir
--transaction-template TRANSACTION_TEMPLATE
dir to save transaction content
--filename FILENAME output filename, leave blank to set default
From api responed data, you can extract dataset to train text-recognization model:
python tools/export-dataset.py
usage: extract-dataset.py [-h] [--respone-dir RESPONE_DIR] [-a OUTPUT_ANNOTATION] [-i OUTPUT_IMAGE_DIR]
optional arguments:
-h, --help show this help message and exit
--respone-dir RESPONE_DIR
dir to api respone
-a OUTPUT_ANNOTATION, --output-annotation OUTPUT_ANNOTATION
path to save annotation file
-i OUTPUT_IMAGE_DIR, --output-image-dir OUTPUT_IMAGE_DIR
path to save annotation file
- Dataset of first 1000 pages lalebed by google-ocr (~336k): Google Drive
- Tips: you may want to balance data text type before extract
18107 transaction statement pages have been extracted from pdf format: Google Drive - Accuracy >99%.