如何提升韩文图像OCR文本提取准确率？EasyOCR优化咨询

当前使用EasyOCR处理韩文图像时识别结果不佳，目标文本为**"부동산 매매 계약서"，但实际输出为"부 동 산 떼 더 겨 약 서"**。需探索优化方法提升准确率，重点确认是否可通过提升图像分辨率改善效果。

用户当前实现代码：

!pip install easyocr
import cv2
import easyocr
import numpy as np
def extract_text_from_image(image):
    # Read the image
    #image = cv2.imread(image_path)

    # Convert the image to grayscale
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    # Apply adaptive thresholding to create a binary image
    _, binary = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)

    # Find contours in the binary image
    contours, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    # Find the contour with the maximum area (foreground)
    max_contour = max(contours, key=cv2.contourArea)

    # Create a mask for the foreground contour
    mask = np.zeros_like(binary)
    cv2.drawContours(mask, [max_contour], 0, 255, -1)

    # Apply the mask to the original image
    preprocessed_image = cv2.bitwise_and(image, image, mask=mask)

    # Initialize the EasyOCR reader
    reader = easyocr.Reader(['en','ko'])

    # Convert the preprocessed image to grayscale
    preprocessed_gray = cv2.cvtColor(preprocessed_image, cv2.COLOR_BGR2GRAY)

    # Perform OCR on the preprocessed image
    results = reader.readtext(preprocessed_gray)

    # Concatenate the extracted text into a single line
    extracted_text = ''
    for result in results:
        extracted_text += result[1] + ' '

    return extracted_text.strip()

img = cv2.imread(file_path)
text = extract_text_from_image(img)

一、提升分辨率对EasyOCR的作用 #

提升图像分辨率确实能有效改善EasyOCR的识别效果，尤其针对低清晰度、文字边缘模糊的图像。更高的分辨率可让文字边缘更锐利，帮助模型捕捉字符细节。实现方式如下：

# 在预处理阶段添加分辨率提升步骤
scale_factor = 2  # 图像放大2倍
high_res_img = cv2.resize(image, None, fx=scale_factor, fy=scale_factor, interpolation=cv2.INTER_LANCZOS4)

二、其他针对性优化手段 #

结合现有预处理流程，可通过以下方向进一步优化：

• 简化预处理流程：当前的轮廓提取+掩码操作可能破坏韩文紧凑字符的完整性，可改用自适应阈值直接处理灰度图：

  gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
  binary = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 11, 2)
  

• 韩文优先识别：初始化EasyOCR时将'ko'放在首位，让模型优先聚焦韩文特征：

  reader = easyocr.Reader(['ko', 'en'])
  

• 调整识别参数：在readtext()中设置段落合并、噪声过滤参数，提升文本连贯性：

  results = reader.readtext(preprocessed_gray, detail=0, paragraph=True, min_size=10)
  

• 图像去噪：若图像存在噪声，先通过高斯模糊预处理：

  denoised_img = cv2.GaussianBlur(gray, (3,3), 0)
  

优化后的完整代码示例 #

!pip install easyocr
import cv2
import easyocr
import numpy as np

def extract_text_from_image(image):
    # 提升图像分辨率
    scale_factor = 2
    image = cv2.resize(image, None, fx=scale_factor, fy=scale_factor, interpolation=cv2.INTER_LANCZOS4)
    
    # 灰度转换+自适应阈值处理
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    binary = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 11, 2)
    
    # 初始化EasyOCR，韩文优先
    reader = easyocr.Reader(['ko', 'en'])
    
    # 执行OCR并合并段落文本
    results = reader.readtext(binary, detail=0, paragraph=True)
    
    return ' '.join(results).strip()

img = cv2.imread(file_path)
text = extract_text_from_image(img)

内容的提问来源于stack exchange，提问作者Med FutureXAI