使用easyocr识别图片文字（本地图片和网络图片）

from PIL import Image
import requests
from io import BytesIO
import easyocr
import numpy as np
from pathlib import Path


file_url = r'./img/1.jpg'   # 需识别的图片
file_url = r"****7b00ea.jpg"
split_symbol = ' '          # 默认空格为分隔符
row_space = 15              # 默认字符高度为15px，当识别出来的字符间距超过这个数值时会换行。

def make_reader():
    # 将模型加载到内存中。模型文件地址 C:\Users\用户\.EasyOCR\model
    reader = easyocr.Reader(['ch_sim', 'en'])
    return reader

def url_img_to_bytes(url):
    img = Image.open(BytesIO(requests.get(
        "url.jpg").content))
    # 剪裁出右下角
    #img = img.crop((586, 680, 800, 800))

    img_io_stream = BytesIO()
    img.save(img_io_stream, format='PNG')
    img_bytes = img_io_stream.getvalue()
    
    return img_bytes


def change_to_character(file_url, reader, split_symbol=' ', row_space=15, save_dir='.',local_img = True):
    if local_img:  #本地图片     
        with open(file_url, "rb") as img:
            img_b = img.read()
        result = reader.readtext(img_b, detail=0)
        #result = reader.readtext(img_b, detail=0)  # 只显示文字
    else: #网络图片
        img_bytes = url_img_to_bytes(file_url)
        result = reader.readtext(img_bytes)


    result.sort(key=lambda x: x[0][0][1])  # 按竖直方向，进行排序==>进行分行处理。

    # 按行进行分组
    content = []
    item = [result[0]]  # 首先放入第一个元素
    for i in result[1:]:
        if row_space >= i[0][0][1] - item[-1][0][0][1] >= 0:
            item.append(i)
        else:
            content.append(item)
            item = [i]
    content.append(item)

    filemane = Path(file_url).name.split('.')[0]
    with open(f'{save_dir}/{filemane}.txt', "w", encoding='utf8') as t:
        for i in content:                     # i 为每一行的内容
            i.sort(key=lambda x: x[0][0][0])  # 对每行的内容进行先后排序
            for r in i:
                # print(r)
                t.write(r[1] + split_symbol)
            t.write("\n")
    return content


if __name__ == "__main__":
    content = change_to_character(file_url,  make_reader(),local_img = False)

注意本地需要先把模型保存到：

将模型加载到内存中。模型文件地址 C:\Users\你的用户\.EasyOCR\model