使用Python实现pdf转图片再进行OCR识别

import os
from pdf2image import convert_from_path

# PDF文件路径
pdf_path = '/Users/xxx/2022.pdf'
# 输出图片的文件夹
output_folder = './output_images2022'
# 输出图片的命名格式
output_name = 'page'

# 如果输出文件夹不存在，创建它
if not os.path.exists(output_folder):
    os.makedirs(output_folder)

# 将PDF转换为图像列表，设置分辨率为300 DPI
images = convert_from_path(pdf_path, dpi=300)

# 保存每一页为PNG图片
for i, image in enumerate(images):
    image.save(f'{output_folder}/{output_name}_{i+1}.png', 'PNG')

from PIL import ImageEnhance
import pytesseract
from PIL import Image
from openpyxl import Workbook

# 配置 Tesseract 的路径（如果需要）
# pytesseract.pytesseract.tesseract_cmd = r'/usr/local/bin/tesseract'  # Mac 的路径
# pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files\Tesseract-OCR\tesseract.exe'  # Windows 的路径

# 打开图片
# image_path = "/Users/xxx/page_3.png"  # 替换为你的图片路径


def enhance_image(img):
    img = img.convert('L')  # 转灰度
    img = ImageEnhance.Contrast(img).enhance(2.0)
    return img


def allimngs(image_path):

    image = Image.open(image_path)

    image = enhance_image(image)

    # 使用 pytesseract 进行 OCR
    text = pytesseract.image_to_string(image, lang="chi_sim")  # 中文

    # # 打印提取的文本
    # print("提取的文本：")
    # print(text.replace(' ', ''))

    return text.replace(' ', '')


# 统计子字符串出现次数

class TrieNode:
    def __init__(self):
        self.children = {}
        self.keywords = []


class Trie:
    def __init__(self):
        self.root = TrieNode()

    def insert(self, keyword):
        node = self.root
        for char in keyword:
            if char not in node.children:
                node.children[char] = TrieNode()
            node = node.children[char]
        node.keywords.append(keyword)


def count_keywords(text, keywords):
    # 去重关键词以确保唯一性
    keywords = list(set(keywords))

    # 构建Trie树
    trie = Trie()
    for kw in keywords:
        trie.insert(kw)

    # 初始化计数器
    counters = {kw: 0 for kw in keywords}
    i = 0
    n = len(text)

    while i < n:
        current_node = trie.root
        max_len = 0
        current_len = 0
        end_pos = i

        # 寻找从当前位置开始的最长匹配关键词
        for j in range(i, n):
            char = text[j]
            if char in current_node.children:
                current_node = current_node.children[char]
                current_len += 1
                if current_node.keywords:  # 当前节点是某个关键词的结尾
                    max_len = current_len
                    end_pos = j + 1  # 更新结束位置为当前字符的下一个位置
            else:
                break  # 无后续匹配，退出循环

        if max_len > 0:
            # 更新所有匹配的关键词计数器
            for kw in current_node.keywords:
                counters[kw] += 1
            i = end_pos  # 跳跃到已匹配部分的末尾
        else:
            i += 1  # 无匹配，移动到下一个字符

    return counters


if __name__ == "__main__":
    keywords = ['矮小',
                '安于现状',
                '暗藏',
                '暗淡',
                '暗黑']
    all_text = ''
    workbook = Workbook()
    sheet = workbook.active

    for i in range(108):
        i = i+1
        image_path = f"/Users/xxx/output_images2022/page_{i}.png"
        all_text = all_text + allimngs(image_path)

    all_text = all_text.replace(' ', '').replace('\n', '')

    result = count_keywords(all_text, keywords)

    num = 1

    for k, v in result.items():
        sheet[f'A{num}'] = k
        sheet[f'B{num}'] = v
        print(k, v, num)
        num = num + 1

    workbook.save(filename='2022.xlsx')

pip install paddleocr

from paddleocr import PaddleOCR, draw_ocr
from PIL import Image
 
# 初始化 PaddleOCR
# 参数解释：
# `lang`：指定语言模型，如 'en'（英文）、'ch'（中文）等。
# `use_angle_cls`：是否启用文字方向分类器。
ocr = PaddleOCR(use_angle_cls=True, lang='en')  # 也可以设置为 'ch' 用于中文[^28^]
 
# 指定图片路径
img_path = 'example.jpg'  # 替换为你的图片路径
 
# 执行 OCR 识别
result = ocr.ocr(img_path, cls=True)  # `cls=True` 表示启用方向分类器
 
# 打印识别结果
for line in result:
    print(line)
 
# 可选：绘制识别结果并保存
if result:
    image = Image.open(img_path).convert('RGB')
    boxes = [line[0] for line in result]  # 提取文字框
    txts = [line[1][0] for line in result]  # 提取文字内容
    scores = [line[1][1] for line in result]  # 提取置信度
 
    # 绘制结果
    im_show = draw_ocr(image, boxes, txts, scores, font_path='path/to/PaddleOCR/doc/fonts/simfang.ttf')
    im_show = Image.fromarray(im_show)
    im_show.save('result.jpg')  # 保存绘制后的图片[^28^]

pip install rapidocr_onnxruntime

from rapidocr_onnxruntime import RapidOCR
 
# 初始化 OCR 引擎
ocr = RapidOCR()
 
# 指定图片路径
img_path = 'example.jpg'  # 替换为你的图片路径
 
# 执行识别
result, _ = ocr(img_path)
 
# 提取并打印识别结果（仅数字和字母）
if result:
    for line in result:
        text = line[1]  # 提取文字内容
        # 筛选只包含数字和字母的文本
        if text.isalnum():
            print(text)
else:
    print("未识别到文字")

pip install easyocr

import easyocr
 
reader = easyocr.Reader(['en', 'ch_sim'])  # 支持多语言
img_path = 'example.jpg'
result = reader.readtext(img_path)
for line in result:
    print(line[1])  # 打印识别结果

pip install pytesseract

from PIL import Image
import pytesseract
 
img_path = 'example.jpg'
text = pytesseract.image_to_string(Image.open(img_path), lang='eng')
print(text)  # 打印识别结果

pip install python-doctr

from doctr.models import ocr_predictor
from doctr.io import DocumentFile
 
img_path = 'example.jpg'
doc = DocumentFile.from_images(img_path)
model = ocr_predictor(pretrained=True)
result = model(doc)
for block in result.pages[0].blocks:
    for line in block.lines:
        for word in line.words:
            print(word.value)  # 打印识别结果

pip install pyocr

import pyocr
from PIL import Image
 
tools = pyocr.get_available_tools()
ocr_tool = tools[0]
img_path = 'example.jpg'
text = ocr_tool.image_to_string(Image.open(img_path), lang='eng')
print(text)  # 打印识别结果