img2table 2.0.0

img2table is a table identification and extraction Python Library for PDF and images, based on OpenCV image processing

img2table

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.

Table of contents

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

Installation

The library can be installed via pip:

Command	Description
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[doctr]	For usage with docTR
pip install img2table[surya]	For usage with Surya OCR
pip install img2table[rapidocr]	For usage with RapidOCR
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

Features

• Table identification for images and PDF files, including bounding boxes at the table cell level
• Handling of complex table structures such as merged cells
• 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

Usage

Documents

Images

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

The implemented method to handle skewed/rotated images supports skew angles up to 45° and is based on the publication by Huang, 2020.
Setting the detect_rotation parameter to True, image coordinates and bounding boxes returned by other methods might not correspond to the original image.

PDF

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.

---

OCR

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.

Tesseract

from img2table.ocr import TesseractOCR

ocr = TesseractOCR(n_threads=1,
                   lang="eng",
                   psm=11,
                   tessdata_dir="...")

Parameters

n_threads : int, optional, default 1

Number of concurrent threads used to call Tesseract

lang : str, optional, default "eng"

Lang parameter used in Tesseract for text extraction

psm : int, optional, default 11

PSM parameter used in Tesseract, run tesseract --help-psm for details

tessdata_dir : str, optional, default None

Directory containing Tesseract traineddata files. If None, the TESSDATA_PREFIX env variable is used.

Usage of Tesseract-OCR requires prior installation. Check documentation for instructions.
For Windows users getting environment variable errors, you can check this tutorial

PaddleOCR

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, ...})

Parameters

lang : str, optional, default "en"

Lang parameter used in Paddle for text extraction, check documentation for available languages

kw : dict, optional, default None

Dictionary containing additional keyword arguments passed to the PaddleOCR constructor.

EasyOCR

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, ...})

Parameters

lang : list, optional, default ["en"]

Lang parameter used in EasyOCR for text extraction, check documentation for available languages

kw : dict, optional, default None

Dictionary containing additional keyword arguments passed to the EasyOCR Reader constructor.

docTR

docTR is an open-source OCR based on Deep Learning models.

from img2table.ocr import DocTR

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

Parameters

detect_language : bool, optional, default False

Parameter indicating if language prediction is run on the document

kw : dict, optional, default None

Dictionary containing additional keyword arguments passed to the docTR ocr_predictor method.

RapidOCR

RapidOCR is an open-source OCR based on ONNX Runtime.

from img2table.ocr import RapidOCR

ocr = RapidOCR(params={"Rec.lang_type": ..., "kwarg": kw_value, ...})

Parameters

params : dict, optional, default None

Dictionary containing configuration values passed to the RapidOCR constructor. If Rec.lang_type is not provided, English is used by default.

Surya OCR

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"])

Parameters

langs : list, optional, default ["en"]

Lang parameter used in Surya OCR for text extraction

Google Vision

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)

Parameters

api_key : str, optional, default None

Google Vision API key

timeout : int, optional, default 15

API requests timeout, in seconds

AWS Textract

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")

Parameters

aws_access_key_id : str, optional, default None

AWS access key id

aws_secret_access_key : str, optional, default None

AWS secret access key

aws_session_token : str, optional, default None

AWS temporary session token

region : str, optional, default None

AWS server region

Azure Cognitive Services

from img2table.ocr import AzureOCR

ocr = AzureOCR(endpoint="abc.azure.com",
               subscription_key="***")

Parameters

endpoint : str, optional, default None

Azure Cognitive Services endpoint. If None, inferred from the COMPUTER_VISION_ENDPOINT environment variable.

subscription_key : str, optional, default None

Azure Cognitive Services subscription key. If None, inferred from the COMPUTER_VISION_SUBSCRIPTION_KEY environment variable.

---

Table extraction

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()

# 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,
                                      max_workers=1)

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

implicit_columns : bool, optional, default False

Boolean indicating if implicit columns 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)

max_workers : int, optional, default 1

Number of concurrent workers used for table extraction. Mainly useful for multi-page PDFs.

Method return

The ExtractedTable class is used to model extracted tables from documents.

Attributes

bbox : BBox

Table bounding box, with absolute coordinates and normalized coordinates available via bbox.relative

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

Normalized coordinates (in percentage of image height / width) are also available on the same object:

relative_bbox = cell.bbox.relative
x1 = relative_bbox.x1
y1 = relative_bbox.y1
x2 = relative_bbox.x2
y2 = relative_bbox.y2

Images

extract_tables method from the Image class returns a list of ExtractedTable objects.

output = [ExtractedTable(...), ExtractedTable(...), ...]

PDF

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(...), ...]
}

Excel export

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()

# 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,
            max_workers=1)

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

implicit_columns : bool, optional, default False

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

borderless_tables : bool, optional, default False

Boolean indicating if borderless tables are extracted.

min_confidence : int, optional, default 50

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

max_workers : int, optional, default 1

Number of concurrent workers used for table extraction. Mainly useful for multi-page PDFs.

Returns

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

Examples

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 content: illustrated effect of the parameter implicit_rows/implicit_columns of the extract_tables method

FYI / Caveats

• For a high-level description of the implemented bordered and borderless table detection algorithms, see the table detection algorithms documentation.
• 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 / LLM based solutions.