The OCR (Optical Character Recognition) approach involves using OCR technology to identify and extract text from images. In the context of your project, the OCR approach is used to localize potential signature areas on bank cheques.
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Python-tesseract is an optical character recognition (OCR) tool for python. It will recognize and “read” the text embedded in images.
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OpenCV-Python is a library of Python bindings designed to solve computer vision problems.
Documents / Cheques contain phrases such as “Please sign above”, “Signature”, etc.
Step1: Image is read from the /cheque_images folder only if it is in .jpg format, using openCV module
cv2.imread()
Step 2: Now using pytesseract's inbuilt function we will extract the data from the image.
pytesseract.image_to_data(Image.open('test.png'))
Result of extracting data from image
Step 3: Now we can directly pick the text of the image. We need "Please sign above".
We can easily find these words and get their location in the image from the (left, top, width, height pointers).
Step 4: Once the location of signatures is found, we can crop the image accordingly.
For cropping the image we will again use the opencv module
cv2.rectangle(image, start_point, end_point, color, thickness)
Parameters:
- image: It is the image on which rectangle is to be drawn.
- start_point: It is the starting coordinates of rectangle. The coordinates are represented as tuples of two values i.e. (X coordinate value, Y coordinate value).
- end_point: It is the ending coordinates of rectangle. The coordinates are represented as tuples of two values i.e. (X coordinate value, Y coordinate value).
- color: It is the color of border line of rectangle to be drawn. For BGR, we pass a tuple. eg: (255, 0, 0) for blue color.
- thickness: It is the thickness of the rectangle border line in px. Thickness of -1 px will fill the rectangle shape by the specified color.
Return Value: It returns an image.
Step 5: The resulting images are stored in the Results folder.
