img2table is a table identification and extraction Python Library for PDF and images, based on OpenCV image processing
MIT License
img2table
is a simple, easy to use, table identification and extraction Python Library based on OpenCV image
processing that supports most common image file formats as well as PDF files.
Thanks to its design, it provides a practical and lighter alternative to Neural Networks based solutions, especially for usage on CPU.
The library can be installed via pip:
pip install img2table: Standard installation, supporting Tesseract pip install img2table[paddle]: For usage with Paddle OCR pip install img2table[easyocr]: For usage with EasyOCR pip install img2table[surya]: For usage with Surya OCR pip install img2table[gcp]: For usage with Google Vision OCR pip install img2table[aws]: For usage with AWS Textract OCR pip install img2table[azure]: For usage with Azure Cognitive Services OCR
Images are loaded using the opencv-python
library, supported formats are listed below.
Both native and scanned PDF files are supported.
Images are instantiated as follows :
from img2table.document import Image
image = Image(src,
detect_rotation=False)
PDF files are instantiated as follows :
from img2table.document import PDF
pdf = PDF(src,
pages=[0, 2],
detect_rotation=False,
pdf_text_extraction=True)
PDF pages are converted to images with a 200 DPI for table identification.
img2table
provides an interface for several OCR services and tools in order to parse table content.
If possible (i.e for native PDF), PDF text will be extracted directly from the file and the OCR service/tool will not be called.
from img2table.ocr import TesseractOCR
ocr = TesseractOCR(n_threads=1,
lang="eng",
psm=11,
tessdata_dir="...")
Usage of Tesseract-OCR requires prior installation. Check documentation for instructions. For Windows users getting environment variable errors, you can check this tutorial
PaddleOCR is an open-source OCR based on Deep Learning models. At first use, relevant languages models will be downloaded.
from img2table.ocr import PaddleOCR
ocr = PaddleOCR(lang="en",
kw={"kwarg": kw_value, ...})
# Example of installation with CUDA 11.8
pip install paddlepaddle-gpu==2.5.0rc1.post118 -f https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html
pip install paddleocr img2table
If you get an error trying to run PaddleOCR on Ubuntu, please check this issue for a working solution.
EasyOCR is an open-source OCR based on Deep Learning models. At first use, relevant languages models will be downloaded.
from img2table.ocr import EasyOCR
ocr = EasyOCR(lang=["en"],
kw={"kwarg": kw_value, ...})
docTR is an open-source OCR based on Deep Learning models. In order to be used, docTR has to be installed by the user beforehand. Installation procedures are detailed in the package documentation
from img2table.ocr import DocTR
ocr = DocTR(detect_language=False,
kw={"kwarg": kw_value, ...})
Only available for python >= 3.10 Surya is an open-source OCR based on Deep Learning models. At first use, relevant models will be downloaded.
from img2table.ocr import SuryaOCR
ocr = SuryaOCR(langs=["en"])
Authentication to GCP can be done by setting the standard GOOGLE_APPLICATION_CREDENTIALS
environment variable.
If this variable is missing, an API key should be provided via the api_key
parameter.
from img2table.ocr import VisionOCR
ocr = VisionOCR(api_key="api_key", timeout=15)
When using AWS Textract, the DetectDocumentText API is exclusively called.
Authentication to AWS can be done by passing credentials to the TextractOCR
class.
If credentials are not provided, authentication is done using environment variables or configuration files.
Check boto3
documentation for more details.
from img2table.ocr import TextractOCR
ocr = TextractOCR(aws_access_key_id="***",
aws_secret_access_key="***",
aws_session_token="***",
region="eu-west-1")
from img2table.ocr import AzureOCR
ocr = AzureOCR(endpoint="abc.azure.com",
subscription_key="***")
Multiple tables can be extracted at once from a PDF page/ an image using the extract_tables
method of a document.
from img2table.ocr import TesseractOCR
from img2table.document import Image
# Instantiation of OCR
ocr = TesseractOCR(n_threads=1, lang="eng")
# Instantiation of document, either an image or a PDF
doc = Image(src)
# Table extraction
extracted_tables = doc.extract_tables(ocr=ocr,
implicit_rows=False,
implicit_columns=False,
borderless_tables=False,
min_confidence=50)
NB: Borderless table extraction can, by design, only extract tables with 3 or more columns.
The ExtractedTable
class is used to model extracted tables from documents.
In order to access bounding boxes at the cell level, you can use the following code snippet :
for id_row, row in enumerate(table.content.values()):
for id_col, cell in enumerate(row):
x1 = cell.bbox.x1
y1 = cell.bbox.y1
x2 = cell.bbox.x2
y2 = cell.bbox.y2
value = cell.value
extract_tables
method from the Image
class returns a list of ExtractedTable
objects.
output = [ExtractedTable(...), ExtractedTable(...), ...]
extract_tables
method from the PDF
class returns an OrderedDict
object with page indexes as keys and lists of ExtractedTable
objects.
output = {
0: [ExtractedTable(...), ...],
1: [],
...
last_page: [ExtractedTable(...), ...]
}
Tables extracted from a document can be exported to a xlsx file. The resulting file is composed of one worksheet per extracted table.
Method arguments are mostly common with the extract_tables
method.
from img2table.ocr import TesseractOCR
from img2table.document import Image
# Instantiation of OCR
ocr = TesseractOCR(n_threads=1, lang="eng")
# Instantiation of document, either an image or a PDF
doc = Image(src)
# Extraction of tables and creation of a xlsx file containing tables
doc.to_xlsx(dest=dest,
ocr=ocr,
implicit_rows=False,
implicit_columns=False,
borderless_tables=False,
min_confidence=50)
Several Jupyter notebooks with examples are available :