Before feeding an image to an OCR system, various image processing techniques are applied to improve text visibility and minimize noise or distortions. The basic steps are as follows:

1. Image Acquisition

  • This is the first step, where the image is captured from a source like a scanner, camera, or downloaded from a file. The image could be in various formats such as JPG, PNG, TIFF, etc.

2. Preprocessing

The goal of preprocessing is to prepare the image to make it easier for the OCR engine to read the text. Common preprocessing steps include:

  • Grayscale Conversion: Convert the image to grayscale to simplify the image by removing color information. OCR systems generally work best with grayscale or binary images.

    gray_image = cv2.cvtColor(input_img, cv2.COLOR_BGR2GRAY)

  • Noise Removal: Remove unwanted artifacts and distortions (such as speckles, background noise) that can affect OCR accuracy. This can be done using techniques like:

    • Median Filtering to remove salt-and-pepper noise.
    • Gaussian Smoothing to reduce image noise.
    blurred_image = cv2.GaussianBlur(gray_image, (5, 5), 0)

  • Thresholding: Convert the grayscale image to a binary image, where text appears in white and the background appears in black (or vice versa). This is typically done using methods like Otsu’s thresholding or adaptive thresholding.

    python
_, binary_image = cv2.threshold(gray_image, 128, 255, cv2.THRESH_BINARY)

  • Dilation and Erosion: These morphological operations help in closing gaps between characters and lines, making text more distinct.

    • Erosion: Removes small white noise or gaps.
    • Dilation: Helps in making text thicker and closing small holes inside objects (characters).
    kernel = np.ones((2, 2), np.uint8)
dilated_image = cv2.dilate(binary_image, kernel, iterations=1)

  • Skew Correction: Ensures that text lines are properly aligned by correcting any tilt in the image.

    • Hough Line Transform or other deskewing algorithms can detect the skew angle and rotate the image.
    (h, w) = gray_image.shape[:2]
rotated = cv2.getRotationMatrix2D((w / 2, h / 2), angle, 1.0)
deskewed_image = cv2.warpAffine(binary_image, rotated, (w, h))

3. Text Region Detection (Optional)

  • If the image contains both text and non-text regions (e.g., tables, figures), a region detection algorithm like Connected Component Analysis (CCA) or Edge Detection may be applied to isolate the areas of text.