文档和图片的OCR解析实践

2024-03-13 12:28:43 浏览数 (2)

文档中的图片或图片本身的OCR识别可以通过第三方工具如PaddleOCR和CNOCR来实现，如下是两个识别过程的实践，以及使用Streamlit构建可视化页面的示例。

PaddleOCR

安装PaddleOCR环境和依赖

代码语言：javascript复制
# gpu
conda create -n paddleocr python=3.9 -y
conda activate paddleocr 
pip install paddlepaddle
# pip install paddlepaddle-gpu
pip3 install "paddleocr>=2.6.0.3"
pip install opencv-python
pip install PyMuPDF

PaddleOCR文档参考：https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.7/ppstructure/docs/quickstart.md

使用PaddleOCR解析PDF中的图片，或直接解析图片中的Table

代码语言：javascript复制
def pdf(file):
    table_engine = PPStructure(layout=False, show_log=True)
    pdf_reader = PyPDF2.PdfReader(file)
    file_content = ''
    for page in pdf_reader.pages:
        file_content  = page.extract_text().strip()   'n'
        for image in page.images:
            with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as temp_image:
                temp_image.write(image.data)
                temp_image_path = temp_image.name
            img = cv2.imread(temp_image_path)

            result = table_engine(img)
            # save_structure_res(result, save_folder, os.path.basename(image.name).split('.')[0])
            for line in result:
                file_content  = f"{line['res']['html']}n"
    return file_content


def image(file):
    file_content = ''
    image_file = Image.open(file)
    img = np.array(image_file)
    # img = cv2.imread(img_array)
    result = table_engine(img)
    for line in result:
        file_content  = f"{line['res']['html']}n"
    print('file_content:', file_content)
    return file_content

CNOCR

使用CNOCR进行解析

依赖如下：

代码语言：javascript复制
pip3 install cnocr
pip3 install onnxruntime

代码实现如下，效果一般

代码语言：javascript复制
def image2():
    from cnocr.utils import read_img
    from cnocr import CnOcr

    ocr = CnOcr()
    img_path = 'books.jpg'
    img = read_img(img_path)
    res = ocr.ocr(img)
    for r in res:
        print(f'{r["text"]}, {r["score"]}')

Streamlit构建前端

使用Streamlit构建前端的完整代码如下：

代码语言：javascript复制
import copy
import PyPDF2
import numpy as np
import openai
import streamlit as st
from PIL import Image
import cv2
import tempfile
from paddleocr import PPStructure, save_structure_res

table_engine = PPStructure(layout=False, show_log=True)


def pdf(file):
    pdf_reader = PyPDF2.PdfReader(file)
    file_content = ''
    for page in pdf_reader.pages:
        file_content  = page.extract_text().strip()   'n'
        for image in page.images:
            with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as temp_image:
                temp_image.write(image.data)
                temp_image_path = temp_image.name
            img = cv2.imread(temp_image_path)

            result = table_engine(img)
            # save_structure_res(result, save_folder, os.path.basename(image.name).split('.')[0])
            for line in result:
                file_content  = f"{line['res']['html']}n"
    return file_content


def image(file):
    file_content = ''
    image_file = Image.open(file)
    img = np.array(image_file)
    # img = cv2.imread(img_array)
    result = table_engine(img)
    for line in result:
        file_content  = f"{line['res']['html']}n"
    print('file_content:', file_content)
    return file_content

# 设置OpenAI API凭证
openai.api_key = "sk-xxx"

# 加载聊天记录
if "messages" not in st.session_state:
    st.session_state.messages = []
    st.session_state.hidden_messages = []

# 加载最后一个文件ID
if "last_file_id" not in st.session_state:
    st.session_state.last_file_id = ""

# 上传文件
uploaded_file = st.file_uploader("上传文件：", type=["png", "jpg", "pdf", "excel", "xls", "doc"])
if uploaded_file is not None:
    if uploaded_file.file_id != st.session_state.last_file_id:
        st.session_state.last_file_id = uploaded_file.file_id
        st.session_state.messages = []
        print('uploaded_file:', uploaded_file)
        with st.chat_message("user"):
            st.markdown("分析中，请等待...")
        file_suffix = uploaded_file.type.split("/")[1]
        messages = copy.deepcopy(st.session_state.messages)
        if file_suffix == "png" or file_suffix == "jpg" or file_suffix == "jpeg":
            st.session_state.messages.append({"role": "user", "image": uploaded_file})
            file_content = image(uploaded_file)
        else:
            file_content = pdf(uploaded_file)

        prompt = "你是一个文档助手，我将传递给你一个文档内容，在下次交互时用户才会开始提问，你只需要回答是否明白。下面的所有内容均为文档内容：n"   file_content
        messages.append({"role": "user", "content": prompt})
        st.session_state.hidden_messages.append({"pos": len(st.session_state.messages), "role": "user", "content": prompt})
        print("messages:", messages)
        response = openai.ChatCompletion.create(
            model="gpt-3.5-turbo-16k",
            messages=messages,
        )
        reply = response.choices[0].message.content.strip()
        print('file reply:', reply)
        st.session_state.messages.append({"role": "assistant", "content": "可以开始提问了。"})

        for message in st.session_state.messages:
            with st.chat_message(message["role"]):
                if "content" in message:
                    st.markdown(message["content"])
                if "image" in message:
                    st.image(message["image"], caption='上传的图片')

# 用户输入
if user_input := st.chat_input("请输入内容..."):
    st.session_state.messages.append({"role": "user", "content": user_input})
    messages = []
    for message in st.session_state.messages:
        with st.chat_message(message["role"]):
            if "content" in message:
                st.markdown(message["content"])
                messages.append(message)
            if "image" in message:
                st.image(message["image"], caption='上传的图片')
    for hidden_message in st.session_state.hidden_messages:
        messages.insert(hidden_message["pos"], {"role": hidden_message["role"], "content": hidden_message["content"]})
    print("chat messages:", messages)
    response = openai.ChatCompletion.create(
        model="gpt-3.5-turbo-16k",
        messages=messages,
    )
    reply = response.choices[0].message.content.strip()
    st.session_state.messages.append({"role": "assistant", "content": reply})
    with st.chat_message("assistant"):
        st.markdown(reply)

参考：https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.7/ppstructure/docs/quickstart.md

文字识别 ocrOCR PaddleOCR CNOCR

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