Multiple Configuration Options for Pytesseract OCR
Pytesseract is a powerful OCR tool used widely for extracting text from images. However, it may encounter challenges when tasked with recognizing specific character sets. To overcome these limitations, users often resort to configuring Tesseract with custom parameters.
One common scenario involves configuring Tesseract to accept single digits while excluding other characters. This becomes essential when distinguishing between the number zero and the letter 'O,' which may appear identical in some instances. To achieve this, Tesseract offers multiple configuration options that can be adjusted accordingly.
Using psm and tessedit_char_whitelist Parameters
With the release of Tesseract 4.0.0a, users gain access to a wider range of page segmentation modes (psm values). For scenarios where single character recognition is the primary objective, setting psm to 10 proves effective. This parameter instructs Tesseract to treat the image as a single character.
Additionally, to restrict Tesseract's recognition to numbers only, users can utilize the tessedit_char_whitelist parameter. By specifying a character whitelist, such as 0123456789, Tesseract will exclusively recognize characters within that whitelist.
Sample Usage
The following code demonstrates how to combine the psm and tessedit_char_whitelist parameters in a practical setting:
import pytesseract target = pytesseract.image_to_string(image, lang='eng', boxes=False, \ config='--psm 10 --oem 3 -c tessedit_char_whitelist=0123456789')
In this example, the image variable represents the input image to be processed, and lang='eng' specifies that the text is in English. By setting boxes=False, the function will not output bounding boxes for recognized characters.
The --psm 10 parameter ensures that single character recognition is utilized, while the --oem 3 parameter selects the default OCR engine. Finally, the -c tessedit_char_whitelist=0123456789 parameter restricts recognition to numbers only.
By understanding and leveraging these multiple configuration options, users can effectively tailor Pytesseract's behavior to suit their specific OCR needs, enabling accurate and precise text extraction.
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