Layout detecton

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

Aspose.OCR for .NET can automatically analyze image content and identify the different types of layout blocks within it.

Pass one or more images as aspose.ocr.OcrInput object to aspose.ocr.detect_document_layout method and get the layout blocks as aspose.ocr.LayoutOutput object.

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

aspose.ocr.LayoutOutput

This class stores a layout block detected in an image.

Property	Type	Description
source	string	The full path to the file or URL, if applicable. Empty for images provided as a stream, byte array, or Base64.
page	int	Page number for multi-page images.
paragraphs	Array of ContentArea	Detected paragraphs.
images	Array of ContentArea	Detected illustrations.
headers	Array of ContentArea	Detected headers.
tables	Array of ContentArea	Detected tables.
lists	Array of ContentArea	Detected lists.
captions	Array of ContentArea	Detected captions.
equations	Array of ContentArea	Detected equations.

Aspose.OCR.ContentArea

This class stores a layout block detected in an image.

Property	Type	Description
index	int	The sequential index of the content area, unique within the entire image.
rectangle	aspose.ocr.Rectangle	The bounding rectangle of the content area.

Example

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

### Detect layout on the image

# Instantiate Aspose.OCR API
api = AsposeOcr()
# Add image to the recognition batch
input = OcrInput(InputType.SINGLE_IMAGE)
input.add("source.png")
# Detect layout
results = api.detect_document_layout(input)
# Print result
for result in results:
    print("paragraphs:" + str(len(result.paragraphs)))
    print("images:" + str(len(result.images)))
    print("tables:" + str(len(result.tables)))
    print("headers:" + str(len(result.headers)))
    print("lists:" + str(len(result.lists)))
    print("captions:" + str(len(result.captions)))
    print("equations:" + str(len(result.equations)))

# Recognize only headers
# Set rectangles for recognition
set = RecognitionSettings()
set.recognition_areas = [area.rectangle for area in result.headers] # use paragraphs or any other areas
# Recognize areas (rectangles)
results = api.recognize(input, set)
for result in results:
    print(result.recognition_text)