Language detecton

To use language detection, install (aspose-ocr-language-classification-v1) advanced OCR model to your project.

Aspose.OCR for .NET can automatically identify the following languages and language families used on the provided image using detect_languages method:

• Latin
• Cyrillic
• Arabic
• Chinese
• Japanese
• Korean
• Hindi
• Tamil
• Telugu
• Kannada

The method accepts the collection of images in any of the supported formats and returns them as an array of LanguageDetectionOutput objects with the following properties:

Property	Type	Description
source	string	The full path or URL of the source file. If the file is provided as a MemoryStream object, an array of pixels, or a Base64 string, this value will be empty.
page	int	Page number. When working with single-page images, this value is always 0.
languages	Array of DetectedLanguage objects	Lists the recognition languages detected in the image along with their probabilities.

DetectedLanguage objects contain the following properties:

Property	Type	Description
language	Language	Detected language
accuracy	float	The confidence level of the language detection, which can be used to assess the accuracy.

Example

The following code sample demonstrates how to detect the image languages:

# Instantiate Aspose.OCR API
api = AsposeOcr()
# Add image to the recognition batch
input = OcrInput(InputType.SINGLE_IMAGE)
input.add("source.png")
# Detect languages
result = api.detect_languages(input)
for lang in result[0].languages:
    print(lang.language)
    print(lang.accuracy)

The following code sample demonstrates how to detect the image languages and recognize multilanguage image:

# Instantiate Aspose.OCR API
api = AsposeOcr()
# Add image to the recognition batch
input = OcrInput(InputType.SINGLE_IMAGE)
input.add("source.png")
# Set language detection
set = RecognitionSettings()
set.language = Language.AUTO
set.language_detection_level = LanguageDetectionLevel.BY_WORD
# Detect languages and recognize image     
results = api.recognize(input, set)
for result in results:
    print(result.recognition_text)