How to filter a list, remove lowest probability value, using python

Question

I have the image in which I want to detect the text, I am using easyocr to detect the text. The OCR gives the output bounding box value and probability as shown in the output image. I want to remove the probability which is less than 0.4 of any text detected How can I change that?

Image1

Image 2

The results element gives the output probability of the first text and second text 'AA' as shown in figure. i want to remove the lowest probability text detected.

Output of image1

Output of image2

Requirements

pip install pytesseract

pip install easyocr

Run the code using python main.py -i image1.jpg

# import the necessary packages
from pytesseract import Output
import pytesseract
import argparse
import cv2
from matplotlib import pyplot as plt
import numpy as np
import os
import easyocr
from PIL import ImageDraw, Image



def remove_lines(image):
    result = image.copy()
    gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
    thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]

    # Remove horizontal lines
    horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (40,1))
    remove_horizontal = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, horizontal_kernel, iterations=2)
    cnts = cv2.findContours(remove_horizontal, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    cnts = cnts[0] if len(cnts) == 2 else cnts[1]
    for c in cnts:
        cv2.drawContours(result, [c], -1, (255,255,255), 5)


    # Remove vertical lines
    vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1,40))
    remove_vertical = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, vertical_kernel, iterations=2)
    cnts = cv2.findContours(remove_vertical, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    cnts = cnts[0] if len(cnts) == 2 else cnts[1]
    for c in cnts:
        cv2.drawContours(result, [c], -1, (255,255,255), 5)

    plt.imshow(result)
    plt.show()

    return result



# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True,
    help="path to input image to be OCR'd")
ap.add_argument("-c", "--min-conf", type=int, default=0,
    help="mininum confidence value to filter weak text detection")
args = vars(ap.parse_args())


reader = easyocr.Reader(['ch_sim','en']) # need to run only once to load model into memory



# load the input image, convert it from BGR to RGB channel ordering,
# and use Tesseract to localize each area of text in the input image
image = cv2.imread(args["image"])
# image = remove_lines(image)

results = reader.readtext(image)


print(results)

Answer 1

results=[([[5, 0],[233, 0],[233, 15],[5, 15]],' Ꮎ ]TC T III3 UᎪCU 3', 0.011015821004953916),
 ([[241, 0], [390, 0], [390, 15], [241, 15] ] , 'ᎠA[ [ C 0lᎢ', 0.0023567583563770737),
 ([[2, 16], [46, 16], [46, 42], [2, 42]], 'MM', 0.9965182566504686),
 ([[98, 16], [140, 16], [140, 46], [98, 46]], 'D', 0.9973547096148511),
 ([[182, 16], [220, 16],[220, 44], [182, 44]], 'Y', 0.9971791823074896),
 ([[24, 46], [62, 46], [62, 74], [24, 74]], '62', 0.9999828941291119),
 ([[94, 46], [130, 46], [130, 74], [94, 74]], '26', 0.9997197349619524),
 ([[180, 46], [242, 46], [242, 74], [180, 74]],'1970', 0.999931275844574)]

low_precision = []
for text in results:
    if text[2]<0.5: # precision here
        low_precision.append(text)
for i in low_precision:
    results.remove(i) # remove low precision
print(results)

result:

[([[2, 16], [46, 16], [46, 42], [2, 42]], 'MM', 0.9965182566504686),
 ([[98, 16], [140, 16], [140, 46], [98, 46]], 'D', 0.9973547096148511),
 ([[182, 16], [220, 16], [220, 44], [182, 44]], 'Y', 0.9971791823074896),
 ([[24, 46], [62, 46], [62, 74], [24, 74]], '62', 0.9999828941291119),
 ([[94, 46], [130, 46], [130, 74], [94, 74]], '26', 0.9997197349619524),
 ([[180, 46], [242, 46], [242, 74], [180, 74]], '1970', 0.999931275844574)]