Extracting text from street photos

Aspose.OCR offers a special recognition algorithm for extracting content from images with sparse text and noisy/colored backgrounds. This method significantly improves OCR accuracy in the following business cases:

  • Read text from street photos.
  • 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.

To extract text from such images, use recognize_street_photo() method of AsposeOcr class.

The method takes OcrInput object and returns a RecognitionResult object containing the text from images.

# Instantiate Aspose.OCR API
api = AsposeOcr()
# Add image to the recognition batch
input = OcrInput(InputType.SINGLE_IMAGE)
input.add("source.png")
# Recognize the image
results = api.recognize_street_photo(input)
# Print recognition result
print(results[0].recognition_text)

Live demo

Street photo

Limitations

  • Install Text-in-wild OCR model in order to use this method.
  • The method only supports Latin letters and numbers.
  • This method does not support recognition settings.
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