ExtractTable - API to extract tabular data from images and scanned PDFs
The motivation is to make it easy for developers to extract tabular data from images or scanned PDF files without worrying about the table area, column coordinates, rotation et al.
API Key: All requests to ExtractTable are authorized by an API Key. FREE credits here. The same API Key can also be used for conversions on the browser at Web Pro.
pip install -U ExtractTable
Ok, enough selling. Let the ease in coding do the talk, and the output encourages you to buy credits; put that timer on and count the LOC.
from ExtractTable import ExtractTable
et_sess = ExtractTable(api_key=YOUR_API_KEY) # Replace your VALID API Key here
print(et_sess.check_usage()) # Checks the API Key validity as well as shows associated plan usage
table_data = et_sess.process_file(filepath=Location_of_Image_with_Tables, output_format="df")
# To process PDF, make use of pages ("1", "1,3-4", "all") params in the read_pdf function
table_data = et_sess.process_file(filepath=Location_of_PDF_with_Tables, output_format="df", pages="all")The tutorial available at 
1. Installation
2. Import and check version
3. Create Session & Validate API Key
3.1 Create Session with your API Key
3.2 Validate the Key and check the plan usage
3.3 Check Usage Details
4. Trigger the extraction process
4.1 Accepted Input Types
4.2 Process an IMAGE Input
4.3 Process a PDF Input
4.4 Output options
4.5 Explore session objects
5. Explore the Output
5.1 Output Structure
5.2 Output Details
6. Make Corrections
6.1 Split Merged Rows
6.2 Split Merged Columns
6.3 Fix Decimal Format
6.4 Fix Date Format
7. Helpful Code Snippets
7.1 Get text data
7.2 Table output to Excel
Certainly. Do you know the current ExtractTable users use it for
- Bank Statement
- Medical Records
- Invoice Details
- Tax forms
- Tender Notices
Its up to you now to explore the ways.
check the complete server response of the latest job with et_sess.ServerResponse.json()
{
"JobStatus": <string>, # Status of the triggered Process @ JOB-LEVEL
"Pages": <integer>, # Number of pages processed in this request @ PAGE-LEVEL
"Tables": [<list of key-value objects of table> # List of all tables found @ TABLE-LEVEL
{
"Page": <integer>, ## Page number in which this table is found
"CharacterConfidence": <float>, ## Accuracy of Characters recognized from the input-page
"LayoutConfidence": <float>, ## Accuracy of table layout's design decision
"TableJson": <dict>, ## Table Cell Text in key-value format with index orientation - {row#: {col#: <str>}}
"TableCoordinates": <dict>, ## Top-left & Bottom-right Cell Coordinates - {row#: {col#: <list(x1,y1,x2,y2)>}}
"TableConfidence": <dict> ## Cell level accuracy of detected characters - {row#: {col#: <float>}}
},
{...} ## ... more "Tables" objects
],
"Lines": [<list of key-value objects> # Pagewise Line details @ PAGE-LEVEL
{
"Page": <integer>, # Page number in which the lines are found
"CharacterConfidence": <float>, # Average Accuracy of all Characters recognized from the input-page
"LinesArray": [
<list of key-value objects of line> # Ordered list of lines in this page @ LINE-LEVEL
{
"Line": <str>, ## Detected text of the complete line
"WordsArray": [
<list of key-value objects> ## Word level datails in this line @ WORD-LEVEL
{
"Conf": <float>, ### Accuracy of recognized characters of the word
"Word": <str>, ### Detected text of the word
"Loc": [x1, y1, x2, y2] ### Top-left & Bottom-right coordinates, w.r.t the input-page width-height dimensions
},
{...} ### More "WordsArray" objects
]
},
{...} ## More "LinesArray" objects
]
},
{...} # More Pagewise "Lines" details
]
}Bug reports/fixes are most welcome and greatly appreciated with API credits. For support reach us at pydevs@extracttable.com
This project is licensed under the Apache License 2.0, see the LICENSE file for details.
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