Foxit PDF SDK
FoxitPDFSDKPython2.PDF2OfficeSettingData Class Reference

Inherits FoxitPDFSDKPython2._object.

Public Member Functions

def PDF2OfficeSettingData ()
 Constructor. More...
 
def Set (metrics_data_folder_path, enable_ml_recognition)
 Set value. More...
 

Static Public Attributes

 enable_ml_recognition = _swig_property(_fsdk.PDF2OfficeSettingData_enable_ml_recognition_get, _fsdk.PDF2OfficeSettingData_enable_ml_recognition_set)
 A boolean value which indicates whether enable machine learning-based recognition functionality. More...
 
 metrics_data_folder_path = _swig_property(_fsdk.PDF2OfficeSettingData_metrics_data_folder_path_get, _fsdk.PDF2OfficeSettingData_metrics_data_folder_path_set)
 A valid path of a folder which contains metrics data files. This should not be an empty string. More...
 

Detailed Description

This class represents setting data used for converting PDF to Office(Word, Excel or PowerPoint) format file.

Constructor & Destructor Documentation

◆ PDF2OfficeSettingData()

def FoxitPDFSDKPython2.PDF2OfficeSettingData.PDF2OfficeSettingData ( )

Constructor.

Constructor, with parameters.

Parameters
[in]metrics_data_folder_pathA valid path of a folder which contains metrics data files. This should not be an empty string. These metrics data files are used to simulate the office format document typesetting process during conversion. They are offered in the "res/metrics_data" folder of the Foxit PDF Conversion SDK package.
[in]enable_ml_recognitionA boolean value which indicates whether enable machine learning-based recognition functionality. true means enable machine learning-based recognition functionality to identify borderless tables in PDF documents. In order to convert the tables better, it will draw a black border with a width of 1 on the recognized borderless table. This will be improved in subsequent versions. And this recognition functionality will be executed on the server side and return the relevant results when it is done. false means disable machine learning-based recognition functionality. And the recognition functionality not based on machine learning will be enabled.
Note
Our machine learning-based technology for identifying borderless tables uses HTTPS and requires a network connection to send the images of the rendered PDF pages to our server during the conversion process.

If it is a tagged PDF document, the parameter enable_ml_recognition will have no effect, and the default value is false.

Member Function Documentation

◆ Set()

def FoxitPDFSDKPython2.PDF2OfficeSettingData.Set (   metrics_data_folder_path,
  enable_ml_recognition 
)

Set value.

Parameters
[in]metrics_data_folder_pathA valid path of a folder which contains metrics data files. This should not be an empty string. These metrics data files are used to simulate the office format document typesetting process during conversion. They are offered in the "res/metrics_data" folder of the Foxit PDF Conversion SDK package.
[in]enable_ml_recognitionA boolean value which indicates whether enable machine learning-based recognition functionality. true means enable machine learning-based recognition functionality to identify borderless tables in PDF documents. In order to convert the tables better, it will draw a black border with a width of 1 on the recognized borderless table. This will be improved in subsequent versions. And this recognition functionality will be executed on the server side and return the relevant results when it is done. false means disable machine learning-based recognition functionality. And the recognition functionality not based on machine learning will be enabled.
Note
Our machine learning-based technology for identifying borderless tables uses HTTPS and requires a network connection to send the images of the rendered PDF pages to our server during the conversion process. If it is a tagged PDF document, the parameter enable_ml_recognition will have no effect, and the default value is false.
Returns
None.

Member Data Documentation

◆ enable_ml_recognition

FoxitPDFSDKPython2.PDF2OfficeSettingData.enable_ml_recognition = _swig_property(_fsdk.PDF2OfficeSettingData_enable_ml_recognition_get, _fsdk.PDF2OfficeSettingData_enable_ml_recognition_set)
static

A boolean value which indicates whether enable machine learning-based recognition functionality.

true means enable machine learning-based recognition functionality to identify borderless tables in PDF documents. And this recognition functionality will be executed on the server side and return the relevant results when it is done. In order to convert the tables better, it will draw a black border with a width of 1 on the recognized borderless table. This will be improved in subsequent versions. false means disable machine learning-based recognition functionality. And the recognition functionality not based on machine learning will be enabled.

Note
Our machine learning-based technology for identifying borderless tables uses HTTPS and requires a network connection to send the images of the rendered PDF pages to our server during the conversion process. If it is a tagged PDF document, this parameter will have no effect, and the default value is false.

◆ metrics_data_folder_path

FoxitPDFSDKPython2.PDF2OfficeSettingData.metrics_data_folder_path = _swig_property(_fsdk.PDF2OfficeSettingData_metrics_data_folder_path_get, _fsdk.PDF2OfficeSettingData_metrics_data_folder_path_set)
static

A valid path of a folder which contains metrics data files. This should not be an empty string.

These metrics data files are used to simulate the office format document typesetting process during conversion. They are offered in the "res/metrics_data" folder of the Foxit PDF Conversion SDK package.