How to count the number of text lines in an Image using OpenCV

Key takeaways:

1. OpenCV is a powerful library for image processing tasks, including counting text lines.

2. Converting images to grayscale simplifies the processing and improves performance.

3. The Canny edge detection algorithm is crucial for identifying the edges in images, which is a precursor to line detection.

4. Hough Line Transform allows for the detection of lines in an image, crucial for counting text lines.

5. Adjusting parameters in edge detection and line detection can improve accuracy depending on the image's characteristics.

Counting the number of text lines in an image is a common task in image processing, especially in Optical Character Recognition (OCR) applications. In this Answer, we will explore how to achieve this using Python and OpenCV, a powerful library for computer vision.

Step-by-step implementation

Here’s how you can count the number of text lines in an image:

1. Load the image: The cv2.imread() function reads the image file named 'text.jpg' and stores it in the variable img. This is the starting point for processing the image.

2. Convert to grayscale: The cv2.cvtColor() function is used to convert the loaded image from BGR (Blue, Green, Red) color space to a grayscale image. This simplification helps in reducing the complexity of further processing since we only need to deal with intensity values rather than color.

3. Edge detection: The cv2.Canny() function applies the Canny edge detection algorithm on the grayscale image. The parameters 50 and 100 are the lower and upper thresholds for edge detection, respectively. The apertureSize parameter is set to 3, indicating the size of the Sobel kernel used for finding gradients. The result is stored in the edges variable, which contains the detected edges of the image.

4. Hough Line Transform: The cv2.HoughLinesP() function detects lines in the edge-detected image. Here’s what the parameters mean:

   1. edges: The input image containing the edges detected by the Canny method.

   2. 39: The resolution of the accumulator in pixels (the distance resolution).

   3. np.pi/180: The angle resolution in radians (1 degree converted to radians).

   4. threshold=100: The minimum number of votes required to detect a line. If a certain number of points (votes) from the edges align, a line will be detected.

   5. minLineLength=100: The minimum length of a line that will be detected. Shorter lines will be ignored.

   6. maxLineGap=10: The maximum allowed gap between points on the same line to link them together.

The detected lines are stored in the variable lines, which is an array of line segments.

5. Count detected lines: The number of lines detected by the Hough Line Transform is counted using len(lines), which gives the total count of line segments found in the image.

Complete code implementation

Let's take a look at the following code example, Press the “Run” button to run the code in the Jupyter Notebook.

Note: You may need to modify parameter values based on different image.

Conclusion

Counting the number of text lines in an image using OpenCV is a straightforward yet powerful technique that finds significant application in various fields, including Optical Character Recognition (OCR). This method allows us to effectively identify and quantify text lines in images. Understanding these steps not only enhances one's skill set in image processing but also serves as a foundation for more complex computer vision tasks.