TIEVisionLibrary.createOCR

Declaration

function createOCR(language: PAnsiChar = nil; engine: TIEVisionOCREngine = ievOCRDefault): TIEVisionOCR; overload; safecall;
function createOCR(path: PAnsiChar; language: PAnsiChar; engine: TIEVisionOCREngine = ievOCRDefault): TIEVisionOCR; overload; safecall;
function createOCR(path: PAnsiChar; languages: TIEVisionVectorString; engine: TIEVisionOCREngine = ievOCRDefault): TIEVisionOCR; overload; safecall;

Description

Create an OCR object for the specified language.
Many languages are available as separate files.


Note:
◼A shortcut method for this is available: OCR
◼To find text boxes in photographs, you can use detectTexts
◼To avoid specifying a path for the language file, call SetCurrentDir() with the language path

Demos

Examples

OCR := IEVisionLib.createOCR(IEOCRLanguageList[OCR_English_language].Code);
str := OCR.recognize(ImageEnView1.IEBitmap.GetIEVisionImage(), IEVisionRect(0, 0, 0, 0)).c_str();

OR

lang := 'fra'; // French
if FileExists( IncludeTrailingPathDelimiter( ExtractFilePath( Application.ExeName )) + lang + '.traineddata' ) = False then
  raise Exception.create( 'Language file not found' );
OCR := IEVisionLib.createOCR( lang );
str := OCR.recognize(ImageEnView1.IEBitmap.GetIEVisionImage(), IEVisionRect(0, 0, 0, 0)).c_str();

OR for multiple languages:

var
  langs: TIEVisionVectorString;
langs := IEVisionLib.createVectorString();
langs.push_back( IEOCRLanguageList[ OCR_English_language ].Code );  // load English
langs.push_back( IEOCRLanguageList[ OCR_Italian_language ].Code );  // load Italian
m_OCR := IEVisionLib.createOCR( '', langs );
str := m_OCR.recognize(ImageEnView1.IEBitmap.GetIEVisionImage(), IEVisionRect(0, 0, 0, 0)).c_str();


// Extract text from an image
mOCR := IEVisionLib.createOCR( PAnsiChar( AnsiString( IEOCRLanguageList[OCR_English_Language].Code )) );
mOCR.setSegmentationMode( ievOCRAuto );
Memo1.Text := mOCR.recognize( DestIEViewer.IEBitmap.GetIEVisionImage(), IEVisionRect( DestIEViewer.SelectedRect )).c_str();



Skew Angle Estimation and Correction of Hand
Written, Textual and Large areas of Non-Textual
Document Images: A Novel Approach
D.R.Ramesh Babu
PES Institute of Technology
Bangalore, Karnataka
India
Piyush M Kumat
Research Scholar
PICT, Pune
India
Mahesh D Dhannawat
Research Scholar
PICT, Pune
India
Abstract- Skew angle estimation and correction of a
document page is an important task for document
analysis and optical character recognition (OCR)
applications. Many approaches of skew detection can
process pure textual document images successfully. But
it is a challenging problem to process documents like

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