OCR.py

import cv2 
import numpy as np
import matplotlib.pyplot as plt
from Load_Model import *
import easyocr

detection_threshold = 0.4
region_threshold = 0.6
category_index = label_map_util.create_category_index_from_labelmap(files['LABELMAP'])

def filter_text(region, ocr_result, region_threshold):
    rectangle_size = region.shape[0]*region.shape[1]
    
    plate = [] 
    for result in ocr_result:
        length = np.sum(np.subtract(result[0][1], result[0][0]))
        height = np.sum(np.subtract(result[0][2], result[0][1]))
        
        if length*height / rectangle_size > region_threshold:
            plate.append(result[1])
    return plate

def ocr_it(image, detections):
    
    # Scores, boxes and classes above threhold
    scores = list(filter(lambda x: x> detection_threshold, detections['detection_scores']))
    boxes = detections['detection_boxes'][:len(scores)]
    classes = detections['detection_classes'][:len(scores)]
    
    # Full image dimensions
    width = image.shape[1]
    height = image.shape[0]
    
    # Apply ROI filtering and OCR
    for idx, box in enumerate(boxes):
        roi = box*[height, width, height, width]
        region = image[int(roi[0]):int(roi[2]),int(roi[1]):int(roi[3])]
        reader = easyocr.Reader(['en'])
        ocr_result = reader.readtext(region)
        
        text = filter_text(region, ocr_result, region_threshold)
        
        print(text)
        return text, region