How to detect text on an X-Ray image with OpenCV

Question

I want to detect text on x-ray images. The goal is to extract the oriented bounding boxes as a matrix where each row is a detected bounding box and each row contains the coordinates of all four edges i.e. [x1, x2, y1, y2]. I'm using python 3 and OpenCV 4.2.0.

Here is a sample image:

The string "test word", "a" and "b" should be detected.

I followed this OpenCV tutorial about creating rotated boxes for contours and this stackoverflow answer about detecting a text area in an image.

The resulting boundary boxes should look something like this:

I was able to detect the text, but the result included a lot of boxes without text.

Here is what I tried so far:

img = cv2.imread(file_name)

## Open the image, convert it into grayscale and blur it to get rid of the noise.
img2gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
ret, mask = cv2.threshold(img2gray, 180, 255, cv2.THRESH_BINARY)
image_final = cv2.bitwise_and(img2gray, img2gray, mask=mask)
ret, new_img = cv2.threshold(image_final, 180, 255, cv2.THRESH_BINARY)  # for black text , cv.THRESH_BINARY_INV


kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (3, 3))
dilated = cv2.dilate(new_img, kernel, iterations=6)

canny_output = cv2.Canny(dilated, 100, 100 * 2)
cv2.imshow('Canny', canny_output)

## Finds contours and saves them to the vectors contour and hierarchy.
contours, hierarchy = cv2.findContours(canny_output, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

# Find the rotated rectangles and ellipses for each contour
minRect = [None] * len(contours)
for i, c in enumerate(contours):
    minRect[i] = cv2.minAreaRect(c)
# Draw contours + rotated rects + ellipses

drawing = np.zeros((canny_output.shape[0], canny_output.shape[1], 3), dtype=np.uint8)

for i, c in enumerate(contours):
    color = (255, 0, 255)
    # contour
    cv2.drawContours(drawing, contours, i, color)

    # rotated rectangle
    box = cv2.boxPoints(minRect[i])
    box = np.intp(box)  # np.intp: Integer used for indexing (same as C ssize_t; normally either int32 or int64)
    cv2.drawContours(img, [box], 0, color)

cv2.imshow('Result', img)
cv2.waitKey()

Do I need to run the results through OCR to make sure whether it is text or not? What other approaches should I try?

PS: I'm quite new to computer vision and not familiar with most concepts yet.

Answer 1

I removed the text using the following comand (after the code of above):

gray2 = cv2.cvtColor(extract, cv2.COLOR_BGR2GRAY)
blur2 = cv2.GaussianBlur(gray2, (5,5), 0)
thresh2 = cv2.threshold(blur2, 0, 255, cv2.THRESH_BINARY)[1]
test = cv2.inpaint(original, thresh2, 7, cv2.INPAINT_TELEA)

Answer 2

Here's a simple approach:

1. Obtain binary image. Load image, create blank mask, convert to grayscale, Gaussian blur, then Otsu's threshold

2. Merge text into a single contour. Since we want to extract the text as one piece, we perform morphological operations to connect individual text contours into a single contour.

3. Extract text. We find contours then filter using contour area with cv2.contourArea and aspect ratio using cv2.arcLength + cv2.approxPolyDP. If a contour passes the filter, we find the rotated bounding box and draw this onto our mask.

4. Isolate text. We perform an cv2.bitwise_and operation to extract the text.

---

Here's a visualization of the process. Using this screenshotted input image (since your provided input image was connected as one image):

Input image -> Binary image

Morph close -> Detected text

Isolated text

Results with the other image

Input image -> Binary image + morph close

Detected text -> Isolated text

Code

import cv2
import numpy as np

# Load image, create mask, grayscale, Gaussian blur, Otsu's threshold
image = cv2.imread('1.png')
original = image.copy() 
blank = np.zeros(image.shape[:2], dtype=np.uint8)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (5,5), 0)
thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]

# Merge text into a single contour
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5,5))
close = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel, iterations=3)

# Find contours
cnts = cv2.findContours(close, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    # Filter using contour area and aspect ratio
    x,y,w,h = cv2.boundingRect(c)
    area = cv2.contourArea(c)
    ar = w / float(h)
    if (ar > 1.4 and ar < 4) or ar < .85 and area > 10 and area < 500:
        # Find rotated bounding box
        rect = cv2.minAreaRect(c)
        box = cv2.boxPoints(rect)
        box = np.int0(box)
        cv2.drawContours(image,[box],0,(36,255,12),2)
        cv2.drawContours(blank,[box],0,(255,255,255),-1)

# Bitwise operations to isolate text
extract = cv2.bitwise_and(thresh, blank)
extract = cv2.bitwise_and(original, original, mask=extract)

cv2.imshow('thresh', thresh)
cv2.imshow('image', image)
cv2.imshow('close', close)
cv2.imshow('extract', extract)
cv2.waitKey()