aspose-ocr-models-textinwild-python-net 23.12.1

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Aspose.OCR.Models.TextInWild for Python via .NET This extension to Aspose.OCR for Python via .NET adds a specialized recognition model and methods to accurately extract text from street photos, traffic camera images, ID cards, driver licenses, and other images with sparse text and noisy/colored backgrounds. This is useful for improving OCR accuracy in specific business cases:

• Segment and identify road signs and signboards within street images.
• Locate price tags and interpret the extracted text as prices.
• Find and aggregate regions of interest on food labels, such as nutritional information or ingredient lists.
• Identify and analyze car license plates.
• Extract text from menus and catalogs.

Check out the Landing Pages of Aspose.OCR for Python via .NET for a more detailed description of the features and possibilities of the library.

##Important considerations:

• This package requires Aspose.OCR for Python via.NET to function properly. It cannot be used separately from the core API.
• The model only works with Latin letters and numbers.

Get Started

Run pip install aspose-ocr-python-net and pip install aspose-ocr-models-textinwild-python-net to fetch the package. If you already have Aspose.OCR for Python via .NET and want to get the latest version, please run pip install --upgrade aspose-ocr-python-net.

To learn more about Aspose.OCR for Python via .NET and explore the basic requirements and features of the library, check out the following Aspose.OCR for Python via .NET Documentation pages for other use cases.

Code snippet

Aspose.OCR for Python via .NET is extremely easy to use, regardless of the application's scale and complexity. Let's try to create a very simple application that can extract text from images and output it to the console.

1. Install the latest version of the aspose-ocr package using pip.
2. Import aspose.ocr module into the application.
3. Create an instance of AsposeOcr class.
4. Create an instance of OcrInput class and add one or more images to it.
5. Extract text from the street photo using recognize_street_photo method.
6. Output the extracted text to the console.

Full code:

import aspose.ocr as ocr

# Initialize OCR engine
api = AsposeOcr()

# Initialize OCR input
input = OcrInput(InputType.SINGLE_IMAGE)
input.add("1.png")
input.add("2.jpg")

# Recognize images
result = api.recognize_street_photo(input)

# Print result
print(result[0].recognition_text)
print(result[1].recognition_text)

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