前段时间我写了Python识别拖放的PDF文件再转成文本文件-CSDN博客

最近有2点更新，一是有一些pdf文件转换出来的图片是横的，这样也可以识别文字，但是可能会影响效果，另一个是发现有一些文字识别不出来，看了关于提高PaddleOCR识别准确率的一些优化（一）_如何提高paddleocr识别准确率-CSDN博客发现是图片文件的尺寸太大了，为此将其缩小一半再识别。确实提高了识别率。

代码：

# -*- coding: utf-8 -*-
"""
Created on Sun Aug 25 10:42:39 2024

@author: YBK
"""

import tkinter as tk
import windnd
from tkinter.messagebox import showinfo
import os
from PIL import Image
import fitz
from fitz import Document as openPDF
import time
import re
from paddleocr import PaddleOCR
import subprocess

def dec_to_36(num):
    base = [str(x) for x in range(10)] + [chr(x) for x in range(ord('A'),ord("A")+26)]
    # 前者把 0 ~ 9 转换成字符串存进列表 base 里，后者把 A ~ Z 存进列表
    l = []
    if num<0:
        return "-"+dec_to_36(abs(num))
    while True:
        num,rem = divmod(num,36) # 求商 和 留余数
        l.append(base[rem])
        if num == 0:
            return "".join(l[::-1])
        
def nowtime_to_str():
    #将当前时间戳转化为36进制，约6位字符，减少文件名长度
    unix_timestamp = int(time.time())
    return(dec_to_36(unix_timestamp))

def pdf2pic(path, pic_path):
    '''
    # 从pdf中提取图片
    :param path: pdf的路径
    :param pic_path: 图片保存的路径
    :return:
    '''
    t0 = time.perf_counter()
    # 使用正则表达式来查找图片
    checkXO = r"/Type(?= */XObject)"
    checkIM = r"/Subtype(?= */Image)"

    # 打开pdf
    doc = openPDF(path)
    # 图片计数
    imgcount = 0
    lenXREF = doc.xref_length()

    # 打印PDF的信息
    print("文件名:{}, 页数: {}, 对象: {}".format(path, len(doc), lenXREF - 1))

    # 遍历每一个对象
    for i in range(1, lenXREF):
        # 定义对象字符串
        text = doc.xref_object(i)
        isXObject = re.search(checkXO, text)
        # 使用正则表达式查看是否是图片
        isImage = re.search(checkIM, text)
        # 如果不是对象也不是图片，则continue
        if not isXObject or not isImage:
            continue
        imgcount += 1
        # 根据索引生成图像
        pix = fitz.Pixmap(doc, i)
        # 根据pdf的路径生成图片的名称
        # new_name = path.replace('\\', '_') + "_img{}.png".format(imgcount)
        # new_name = new_name.replace(':', '')
        new_name = os.path.basename(path).replace('.pdf', '_') + "img" + str(imgcount).zfill(3) + ".png"
        # 如果pix.n<5,可以直接存为PNG
        if pix.n < 5:
            pix._writeIMG(os.path.join(pic_path, new_name),1,10)
        # 否则先转换CMYK
        else:
            pix0 = fitz.Pixmap(fitz.csRGB, pix)
            pix0._writeIMG(os.path.join(pic_path, new_name),1,10)
            pix0 = None
        # 释放资源
        pix = None
        image = Image.open(os.path.join(pic_path, new_name))
        #对于尺寸大于2000 * 2000的图像，缩放至（h * 0.5，w * 0.5）识别准确率有所提升
        if image.width > 2000 or image.height > 2000:
            new_image = image.resize((int(image.width * 0.5), int(image.height * 0.5)))
            new_image.save(os.path.join(pic_path, new_name))
            print("缩小图片尺寸")
            new_image.close()
        image = Image.open(os.path.join(pic_path, new_name))
        #对于图片宽度大于高度，左旋转
        if image.width > image.height: 
            rotated_img = image.transpose(Image.ROTATE_90)
            print("左旋转")
            rotated_img.save(os.path.join(pic_path, new_name))           
        image.close()
        t1 = time.perf_counter()
        print("运行时间:{}s".format(t1 - t0))
        print("提取了{}张图片".format(imgcount))
def get_file_size(file_path):
    # 获取文件的大小（单位为字节）
    file_size = os.stat(file_path).st_size
    return file_size
def dragged_files(files):
    fileurl = ''
    if len(files) > 1:
        # print("请拖放一个文件！")
        showinfo("提示","请拖放一个文件！")
    else:
        # print(files[0].decode('gbk'))
        fileurl = files[0].decode('gbk')
        # print(os.path.splitext(fileurl)[1])
    if fileurl != '' and os.path.splitext(fileurl)[1] == '.pdf':
        pdfpath = fileurl
        filename0 = os.path.basename(fileurl).replace('.pdf','') + nowtime_to_str()
        # filename0 用于生成文件夹和文件名，为了不重复，在后面加入编码后的时间戳
        pic_path = f'e:\\临时文件夹\\{filename0}\\'
        if not os.path.exists(pic_path):
            os.mkdir(pic_path)
        m = pdf2pic(pdfpath, pic_path)
        pngpath = pic_path
        outtxtpath = 'e:\\临时文件夹\\'+filename0+'.txt'
        ocr = PaddleOCR(use_angle_cls=True, lang="ch") # need to run only once to download and load model into memory
        lines = []
        for filename in os.listdir(pngpath):
            img_path = pngpath+filename
            result = ocr.ocr(img_path, cls=True)
            print(img_path)
            # image = Image.open(img_path).convert('RGB')
            if result[0] is not None:
                boxes = [detection[0] for line in result for detection in line] # Nested loop added
                txts = [detection[1][0] for line in result for detection in line] # Nested loop added
                scores = [detection[1][1] for line in result for detection in line] # Nested loop added
                for box, txt, score in zip(boxes, txts, scores):
                    if score > 0.7:
                        # lines.append(txt.replace('\n',''))
                        lines.append(txt+'\n')
                # lines.append('\n')
        with open(outtxtpath, 'w', encoding='utf-8') as f:
            f.writelines(line for line in lines)
        subprocess.run(['notepad.exe', outtxtpath], check=True)

 
if __name__ == '__main__':
    rootWindow = tk.Tk()
    rootWindow.title("拖放PDF文件识别文字")
    rootWindow.geometry("300x120")
    windnd.hook_dropfiles(rootWindow , func=dragged_files)
    rootWindow.mainloop()