Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/andrealenzi11/py-poppleract
Python library and Web service based on Poppler Pdftotext utility and Tesseract OCR for extracting text from PDF documents
https://github.com/andrealenzi11/py-poppleract
ocr optical-character-recognition pdf-reader pdf-splitting pdf-to-text pdf2text pdftotext poppler poppleract py-poppleract tesseract tesseract-ocr text-extraction
Last synced: 14 days ago
JSON representation
Python library and Web service based on Poppler Pdftotext utility and Tesseract OCR for extracting text from PDF documents
- Host: GitHub
- URL: https://github.com/andrealenzi11/py-poppleract
- Owner: andrealenzi11
- License: gpl-2.0
- Created: 2023-12-04T16:41:17.000Z (11 months ago)
- Default Branch: master
- Last Pushed: 2024-10-18T07:47:03.000Z (26 days ago)
- Last Synced: 2024-10-19T23:35:53.801Z (24 days ago)
- Topics: ocr, optical-character-recognition, pdf-reader, pdf-splitting, pdf-to-text, pdf2text, pdftotext, poppler, poppleract, py-poppleract, tesseract, tesseract-ocr, text-extraction
- Language: Python
- Homepage:
- Size: 202 KB
- Stars: 9
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
Awesome Lists containing this project
README
# py-poppleract
Python library and Web service based on **Poppler Pdftotext** utility and **Tesseract OCR**
for extracting text from PDF documents.
Often, many pdf documents are of mixed type and contain:
- machine-readable pages from which text can be extracted with pdf rendering libraries;
- **not** machine-readable pages (images) from which text can only be extracted with OCR engines.
With this tool (**Poppleract**), you can extract text from mixed documents efficiently and easily .
## How to extract text from an input pdf through the web service
### Run the Poppleract services
Build the Poppleract services image or pull it from Docker Hub:
```console
docker pull andrealenzi/poppleract-services:0.0.8
```
Run and expose on the desired port (ex. 50000) the Poppleract services:
```console
docker run -it --rm -p 50000:8080 andrealenzi/poppleract-services:0.0.8
```
See the APIs documentation:
```url
http://0.0.0.0:50000/docs
http://0.0.0.0:50000/redoc
```
### Call the Text Extraction Service
Manually interact with the Openapi interface (Swagger) on [http://0.0.0.0:50000/docs#/default/extract_text_extract_text_post](http://0.0.0.0:50000/docs#/default/extract_text_extract_text_post)
Or perform a **CURL** to extract text from the specified input pdf doc:
```console
curl -X 'POST' \
'http://0.0.0.0:50000/extract_text?minimum_chars_number=20&raw=false&physical=false&dpi=200&lang=eng&oem=3&psm=3&thresholding_method=0&preserve_interword_spaces=1' \
-H 'accept: application/json' \
-H 'Content-Type: multipart/form-data' \
-F '[email protected];type=application/pdf'
```
Or perform a request with **Python**:
```python3
import requests
headers = {
'accept': 'application/json'
}
params = {
'minimum_chars_number': '20',
'raw': 'false',
'physical': 'false',
'dpi': '200',
'lang': 'eng',
'oem': '3',
'psm': '3',
'tessdata_dir': None,
'thresholding_method': '0',
'preserve_interword_spaces': '1',
}
files = {
'input_file': ('doc1.pdf', open('doc1.pdf', 'rb'), 'application/pdf')
}
response = requests.post(url='http://0.0.0.0:50000/extract_text',
params=params,
headers=headers,
files=files)
print(response.json())
```
Response body:
```json
{
"file_name": "doc1.pdf",
"file_size_mb": 0.1771,
"num_extracted_chars": 762,
"extracted_text": "INTERNAL\n\nTEST DOC 1\nThis is a pdf document for test.\n\nThis page is machine-readable.\nThe second page of this document is NOT machine-readable, but it represents an image with text.\nThe third page of this document is again machine-readable.\n\nQwertyuiop\nAsdfghjkl\nZxcvbnm\n\nQWERTYUIOP\nASDFGHJKL\nZXCVBNM\n\n\x0c\n\n\n\nINTERNAL\n\nThis is a lot of 12 point text to test the\nocr code and see if it works on all types\nof file format.\n\nThe quick brown dog jumped over the\nlazy fox. The quick brown dog jumped\nover the lazy fox. The quick brown dog\njumped over the lazy fox. The quick\nbrown dog jumped over the lazy fox.\n\n\n\n\nINTERNAL\n\nThird and final page of this test document.\n\nQwertyuiop\nAsdfghjkl\nZxcvbnm\n\nQWERTYUIOP\nASDFGHJKL\nZXCVBNM\n\n\n\n\x0c"
}
```
## How to use programmatically *PoppleractPdfExtractor*
```python3
"""
Hybrid Approach for extract text from mixed PDFs:
Pdftotext on machine-readable pages + Tesseract OCR on images pages
"""
from poppleract.text_extraction import PoppleractPdfExtractor
hybrid_extr_obj = PoppleractPdfExtractor(
cache_folder="imgs/", # Folder with doc images representing pages
preserve_cache=False # Boolean flag for preserve the folder with doc images or not
)
hybrid_extr_obj.extract_text(
in_pdf_file_path="doc1.pdf", # Input pdf document path
out_txt_file_path="doc1.txt", # Output txt file path
minimum_chars_number=20, # For each page, we apply OCR only if we extract less than this threshold value
raw=False, # Pdftotext parameter to keep strings in content stream order or not
physical=False, # Pdftotext parameter to maintain original physical layout or not
dpi=200, # Dots per Inch (DPI) used by Pdftocairo and Tesseract
lang="eng+ita", # Tesseract langs
oem=3, # Tesseract OCR Engine Mode
psm=3, # Tesseract Page Segmentation Mode
tessdata_dir="/usr/local/share/tessdata/", # Folder with Tesseract languages files
thresholding_method=0, # Tesseract parameter to select image thresholding method
preserve_interword_spaces=1 # Tesseract option to preserve spaces
)
```
## How to use programmatically *PdfSplitter*
```python3
"""
Splitting of an input pdf document in the relative png pages
"""
from poppleract.pdf_splitting import PdfSplitter
pdf_splitter_obj = PdfSplitter()
pdf_splitter_obj.split_pdf_to_images(
in_pdf_file_path="doc1.pdf",
out_images_directory_path="imgs/",
dpi=200,
img_exportation_format="png",
use_pdf_to_cairo=True,
first_page=1,
last_page=3,
output_filename_prefix="page"
)
```