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.

• Installation
• Features
• Supported file formats
• Usage
  • Documents
    • Images
    • PDF
  • Supported OCRs
  • Table extraction
  • Excel export
• Examples
• Caveats / FYI

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[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

• Table identification for images and PDF files, including bounding boxes at the table cell level
• Handling of complex table structures such as merged cells
• Handling of implicit rows - see example
• Table content extraction by providing support for OCR services / tools
• Extracted tables are returned as a simple object, including a Pandas DataFrame representation
• Export extracted tables to an Excel file, preserving their original structure

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)

Parameters

src : str, pathlib.Path, bytes or io.BytesIO, required

Image source

detect_rotation : bool, optional, default False

Detect and correct skew/rotation of the image

PDF files are instantiated as follows :

from img2table.document import PDF

pdf = PDF(src, 
          pages=[0, 2],
          detect_rotation=False,
          pdf_text_extraction=True)

Parameters

src : str, pathlib.Path, bytes or io.BytesIO, required

PDF source

pages : list, optional, default None

List of PDF page indexes to be processed. If None, all pages are processed

detect_rotation : bool, optional, default False

Detect and correct skew/rotation of extracted images from the PDF

pdf_text_extraction : bool, optional, default True

Extract text from the PDF file for native PDFs

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="...")

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

from img2table.ocr import EasyOCR

ocr = EasyOCR(lang=["en"],
              kw={"kwarg": kw_value, ...})

from img2table.ocr import DocTR

ocr = DocTR(detect_language=False,
            kw={"kwarg": kw_value, ...})

from img2table.ocr import VisionOCR

ocr = VisionOCR(api_key="api_key", timeout=15)

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,
                                      borderless_tables=False,
                                      min_confidence=50)

Parameters

ocr : OCRInstance, optional, default None

OCR instance used to parse document text. If None, cells content will not be extracted

implicit_rows : bool, optional, default False

Boolean indicating if implicit rows should be identified - check related example

borderless_tables : bool, optional, default False

Boolean indicating if borderless tables are extracted on top of bordered tables.

min_confidence : int, optional, default 50

Minimum confidence level from OCR in order to process text, from 0 (worst) to 99 (best)

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.

Attributes

bbox : BBox

Table bounding box

title : str

Extracted title of the table

content : OrderedDict

Dict with row indexes as keys and list of TableCell objects as values

df : pd.DataFrame

Pandas DataFrame representation of the table

html : str

HTML representation of the table

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,
            borderless_tables=False,
            min_confidence=50)

Parameters

dest : str, pathlib.Path or io.BytesIO, required

Destination for xlsx file

ocr : OCRInstance, optional, default None

OCR instance used to parse document text. If None, cells content will not be extracted

implicit_rows : bool, optional, default False

Boolean indicating if implicit rows should be identified - check related example

borderless_tables : bool, optional, default False

Boolean indicating if borderless tables are extracted. It requires to provide an OCR to the method in order to be performed - feature in alpha version

min_confidence : int, optional, default 50

Minimum confidence level from OCR in order to process text, from 0 (worst) to 99 (best)

Returns

If a io.BytesIO buffer is passed as dest arg, it is returned containing xlsx data

Several Jupyter notebooks with examples are available :

• Basic usage: generic library usage, including examples with images, PDF and OCRs
• Borderless tables: specific examples dedicated to the extraction of borderless tables
• Implicit rows: illustrated effect of the parameter implicit_rows of the extract_tables method

• For table extraction, results are highly dependent on OCR quality. By design, tables where no OCR data can be found are not returned.
• The library is tailored for usage on documents with white/light background. Effectiveness can not be guaranteed on other type of documents.
• Table detection using only OpenCV processing can have some limitations. If the library fails to detect tables, you may check CNN based solutions like CascadeTabNet or the PaddleOCR implementation.