Extracting text from street photos
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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 OcrOutput
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
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.