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import re
import cv2
import numpy as np
import pytesseract
from pytesseract import Output
import gradio as gr
#image preprocessing function
#grayscale image
def get_grayscale(image):
return cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
# noise removal
def remove_noise(image):
return cv2.medianBlur(image,5)
#thresholding
def thresholding(image):
return cv2.threshold(image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
#dilation
def dilate(image):
kernel = np.ones((5,5),np.uint8)
return cv2.dilate(image, kernel, iterations = 1)
#erosion
def erode(image):
kernel = np.ones((5,5),np.uint8)
return cv2.erode(image, kernel, iterations = 1)
#opening - erosion followed by dilation
def opening(image):
kernel = np.ones((5,5),np.uint8)
return cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel)
#canny edge detection
def canny(image):
return cv2.Canny(image, 100, 200)
#skew correction
def deskew(image):
coords = np.column_stack(np.where(image > 0))
angle = cv2.minAreaRect(coords)[-1]
if angle < -45:
angle = -(90 + angle)
else:
angle = -angle
(h, w) = image.shape[:2]
center = (w // 2, h // 2)
M = cv2.getRotationMatrix2D(center, angle, 1.0)
rotated = cv2.warpAffine(image, M, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)
return rotated
#template matching
def match_template(image, template):
return cv2.matchTemplate(image, template, cv2.TM_CCOEFF_NORMED)
def extract_text_from_image(img):
# Convert To PIL Image
im_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
gray = get_grayscale(im_rgb)
noise_remove = remove_noise(gray)
# thresh = thresholding(noise_remove)
text = pytesseract.image_to_string(noise_remove)
return text
app = gr.Interface(
fn=extract_text_from_image,
inputs=gr.Image(label="upload image"),
outputs= gr.Textbox()
)
app.launch() |