# -*- coding: utf-8 -*-

import json
import base64
from tencentcloud.common import credential
from tencentcloud.common.profile.client_profile import ClientProfile
from tencentcloud.common.profile.http_profile import HttpProfile
from tencentcloud.common.exception.tencent_cloud_sdk_exception import TencentCloudSDKException
from tencentcloud.ocr.v20181119 import ocr_client, models

try:
    # 1. 读取图片并转换为Base64编码
    with open("本地图片路径.jpg", "rb") as image_file:  # 替换为你的图片路径
        image_base64 = base64.b64encode(image_file.read()).decode('utf-8')

    # 2. 初始化认证对象和客户端
    cred = credential.Credential("YOUR_SECRET_ID", "YOUR_SECRET_KEY")
    httpProfile = HttpProfile()
    httpProfile.endpoint = "ocr.tencentcloudapi.com"
    clientProfile = ClientProfile()
    clientProfile.httpProfile = httpProfile
    client = ocr_client.OcrClient(cred, "ap-guangzhou", clientProfile)  # 替换为你的地域

    # 3. 构建请求
    req = models.RecognizeTableAccurateOCRRequest()
    params = {
        "ImageBase64": image_base64
    }
    req.from_json_string(json.dumps(params))

    # 4. 发送请求并打印结果
    resp = client.RecognizeTableAccurateOCR(req)
    print(resp.to_json_string())

except TencentCloudSDKException as err:
    print(err)

2. 将 JSON 结果转换为 Excel

获取到 API 返回的 JSON 数据后，可以利用 Python 的 openpyxl 库将其解析并存为 Excel 文件。

示例代码：JSON 转 Excel

# -*- coding: utf-8 -*-

import json
import openpyxl
from openpyxl.styles import Alignment

# 假设 data 是从API获取的JSON解析后的字典
# data = json.loads(resp.to_json_string())

# 提取表格数据
main_table = data['TableDetections']  # 通常主表格在索引1
cells = main_table['Cells']

# 创建Excel工作簿和工作表
wb = openpyxl.Workbook()
ws = wb.active
ws.title = "识别结果"

# 填充数据并处理合并单元格
merged_cells = set()
for cell in cells:
    row_start = cell['RowTl'] + 1
    col_start = cell['ColTl'] + 1
    row_end = cell['RowBr'] + 1
    col_end = cell['ColBr'] + 1
    text = cell['Text'].replace('\n', ' ')

    # 填充左上角单元格
    current_cell = ws.cell(row=row_start, column=col_start, value=text)
    current_cell.alignment = Alignment(horizontal='center', vertical='center', wrap_text=True)

    # 记录合并信息
    if row_end > row_start or col_end > col_start:
        merge_range = f"{openpyxl.utils.get_column_letter(col_start)}{row_start}:{openpyxl.utils.get_column_letter(col_end-1)}{row_end-1}"
        if merge_range not in merged_cells:
            ws.merge_cells(merge_range)
            merged_cells.add(merge_range)

# 保存文件
output_file = "表格识别结果.xlsx"
wb.save(output_file)
print(f"Excel文件已生成: {output_file}")

3. 批量识别图片并生成 Excel

为了提高效率，可以编写脚本来批量处理一个文件夹中的所有图片，并将每张图片的识别结果保存到同一个 Excel 文件的不同工作表中。

单线程版

适合图片数量较少的场景，代码简单易懂。

示例代码：单线程批量处理

   import json
   import base64
   import os
   from tencentcloud.common import credential
   from tencentcloud.common.profile.client_profile import ClientProfile
   from tencentcloud.common.profile.http_profile import HttpProfile
   from tencentcloud.common.exception.tencent_cloud_sdk_exception import TencentCloudSDKException
   from tencentcloud.ocr.v20181119 import ocr_client, models
   import openpyxl
   from openpyxl.styles import Alignment
   import time
   import re
   
   # 替换为你的腾讯云API密钥
   SECRET_ID = ""
   SECRET_KEY = ""
   # 设置图片目录
   IMAGE_DIR = "images"  # 替换为你的图片目录路径
   # 设置输出Excel文件路径
   OUTPUT_EXCEL = "所有表格数据.xlsx"  # 输出Excel文件名
   
   # 初始化OCR客户端（只初始化一次）
   def init_ocr_client():
       cred = credential.Credential(SECRET_ID, SECRET_KEY)
       httpProfile = HttpProfile()
       httpProfile.endpoint = "ocr.tencentcloudapi.com"
       clientProfile = ClientProfile()
       clientProfile.httpProfile = httpProfile
       return ocr_client.OcrClient(cred, "ap-guangzhou", clientProfile)
   
   # OCR识别函数
   def recognize_table(image_path, ocr_client):
       try:
           with open(image_path, "rb") as image_file:
               image_base64 = base64.b64encode(image_file.read()).decode('utf-8')
           
           req = models.RecognizeTableAccurateOCRRequest()
           params = {"ImageBase64": image_base64}
           req.from_json_string(json.dumps(params))
           
           resp = ocr_client.RecognizeTableAccurateOCR(req)
           return json.loads(resp.to_json_string())
       
       except Exception as e:
           print(f"处理图片 {image_path} 时出错: {str(e)}")
           return None
   
   # 创建安全的Excel工作表名称
   def create_safe_sheet_name(name, existing_names):
       # 移除非法字符
       safe_name = re.sub(r'[\\/*?:\[\]]', '', name)
       
       # 截断到31个字符
       safe_name = safe_name[:31]
       
       # 确保名称唯一
       base_name = safe_name
       counter = 1
       while safe_name in existing_names or not safe_name.strip():
           safe_name = f"{base_name}_{counter}"
           counter += 1
           if counter > 100:  # 防止无限循环
               safe_name = f"Sheet_{len(existing_names) + 1}"
               break
       
       return safe_name
   
   # 将OCR结果转换为Excel工作表
   def ocr_to_excel_sheet(wb, result, sheet_name):
       if not result or 'TableDetections' not in result or len(result['TableDetections']) < 2:
           print(f"无法提取表格数据: {sheet_name}")
           return False
       
       try:
           # 创建新工作表
           ws = wb.create_sheet(title=sheet_name)
           
           # 提取主表格（第二个表格，索引1）
           main_table = result['TableDetections'][1]
           cells = main_table['Cells']
           
           # 设置列宽
           column_widths = [8, 25, 8, 40, 10, 10, 10, 10, 10]
           for i, width in enumerate(column_widths, 1):
               col_letter = openpyxl.utils.get_column_letter(i)
               ws.column_dimensions[col_letter].width = width
           
           # 处理单元格
           merged_cells = []
           max_row = 0
           max_col = 0
           
           for cell in cells:
               row_start = cell['RowTl'] + 1
               row_end = cell['RowBr']
               col_start = cell['ColTl'] + 1
               col_end = cell['ColBr']
               
               # 更新最大行列
               max_row = max(max_row, row_end)
               max_col = max(max_col, col_end)
               
               # 处理文本：删除所有换行符
               text = cell['Text'].replace('\n', '')
               
               # 填充左上角单元格
               ws.cell(row=row_start, column=col_start, value=text)
               
               # 记录需要合并的单元格
               if row_end > row_start or col_end > col_start:
                   merged_cells.append((row_start, row_end, col_start, col_end))
           
           # 应用合并单元格
           for merge in merged_cells:
               row_start, row_end, col_start, col_end = merge
               ws.merge_cells(
                   start_row=row_start, end_row=row_end,
                   start_column=col_start, end_column=col_end
               )
           
           # 设置居中和自动换行
           for row in ws.iter_rows(min_row=1, max_row=max_row, min_col=1, max_col=max_col):
               for cell in row:
                   cell.alignment = Alignment(
                       horizontal='center', 
                       vertical='center',
                       wrap_text=True
                   )
           
           return True
       
       except Exception as e:
           print(f"生成工作表时出错: {str(e)}")
           return False
   
   # 主处理函数
   def process_images(image_dir, output_excel):
       # 初始化OCR客户端
       ocr_client = init_ocr_client()
       
       # 获取图片列表
       image_files = []
       for file in os.listdir(image_dir):
           if file.lower().endswith(('.png', '.jpg', '.jpeg', '.bmp')):
               image_files.append(os.path.join(image_dir, file))
       
       if not image_files:
           print(f"在目录 {image_dir} 中未找到图片文件")
           return
       
       print(f"找到 {len(image_files)} 张图片需要处理")
       
       # 创建Excel工作簿
       wb = openpyxl.Workbook()
       # 删除默认创建的工作表
       if 'Sheet' in wb.sheetnames:
           del wb['Sheet']
       
       # 处理每张图片
       success_count = 0
       sheet_names = []  # 记录已使用的工作表名称
       
       for i, image_path in enumerate(image_files):
           filename = os.path.basename(image_path)
           base_name = os.path.splitext(filename)[0]
           
           # 创建安全的工作表名称
           sheet_name = create_safe_sheet_name(base_name, sheet_names)
           sheet_names.append(sheet_name)
           
           print(f"正在处理 ({i+1}/{len(image_files)}): {filename} -> 工作表: {sheet_name}")
           
           # OCR识别
           start_time = time.time()
           result = recognize_table(image_path, ocr_client)
           ocr_time = time.time() - start_time
           
           if result:
               # 转换为Excel工作表
               excel_start = time.time()
               success = ocr_to_excel_sheet(wb, result, sheet_name)
               excel_time = time.time() - excel_start
               
               if success:
                   success_count += 1
                   print(f"  处理成功! OCR耗时: {ocr_time:.2f}s, 工作表生成耗时: {excel_time:.2f}s")
               else:
                   print(f"  生成工作表失败")
           else:
               print(f"  OCR识别失败")
           
           # 避免请求过于频繁
           if i < len(image_files) - 1:
               time.sleep(1)  # 1秒间隔
       
       # 保存Excel文件
       wb.save(output_excel)
       print(f"\n处理完成! 成功处理 {success_count}/{len(image_files)} 张图片")
       print(f"结果已保存到: {output_excel}")
   
   # 启动批量处理
   if __name__ == "__main__":
       process_images(IMAGE_DIR, OUTPUT_EXCEL)

多线程版

利用多线程并发请求，显著提升处理大量图片时的效率。

示例代码：批量识别（多线程）

import json
import base64
import os
import time
import re
from tencentcloud.common import credential
from tencentcloud.common.profile.client_profile import ClientProfile
from tencentcloud.common.profile.http_profile import HttpProfile
from tencentcloud.common.exception.tencent_cloud_sdk_exception import TencentCloudSDKException
from tencentcloud.ocr.v20181119 import ocr_client, models
import openpyxl
from openpyxl.styles import Alignment
from concurrent.futures import ThreadPoolExecutor, as_completed

# 替换为你的腾讯云API密钥
SECRET_ID = ""
SECRET_KEY = ""
# 设置图片目录
IMAGE_DIR = "images"  # 替换为你的图片目录路径
# 设置输出Excel文件路径
OUTPUT_EXCEL = "所有表格数据.xlsx"  # 输出Excel文件名

# 初始化OCR客户端（只初始化一次）
def init_ocr_client():
    cred = credential.Credential(SECRET_ID, SECRET_KEY)
    httpProfile = HttpProfile()
    httpProfile.endpoint = "ocr.tencentcloudapi.com"
    clientProfile = ClientProfile()
    clientProfile.httpProfile = httpProfile
    return ocr_client.OcrClient(cred, "ap-guangzhou", clientProfile)

# OCR识别函数
def recognize_table(image_path, ocr_client):
    try:
        with open(image_path, "rb") as image_file:
            image_base64 = base64.b64encode(image_file.read()).decode('utf-8')
        
        req = models.RecognizeTableAccurateOCRRequest()
        params = {"ImageBase64": image_base64}
        req.from_json_string(json.dumps(params))
        
        resp = ocr_client.RecognizeTableAccurateOCR(req)
        return json.loads(resp.to_json_string())
    
    except Exception as e:
        print(f"处理图片 {image_path} 时出错: {str(e)}")
        return None

# 创建安全的Excel工作表名称
def create_safe_sheet_name(name, existing_names):
    # 移除非法字符
    safe_name = re.sub(r'[\\/*?:\[\]]', '', name)
    
    # 截断到31个字符
    safe_name = safe_name[:31]
    
    # 确保名称唯一
    base_name = safe_name
    counter = 1
    while safe_name in existing_names or not safe_name.strip():
        safe_name = f"{base_name}_{counter}"
        counter += 1
        if counter > 100:  # 防止无限循环
            safe_name = f"Sheet_{len(existing_names) + 1}"
            break
    
    return safe_name

# 将OCR结果转换为Excel工作表
def ocr_to_excel_sheet(wb, result, sheet_name):
    if not result or 'TableDetections' not in result or len(result['TableDetections']) < 2:
        print(f"无法提取表格数据: {sheet_name}")
        return False
    
    try:
        # 创建新工作表
        ws = wb.create_sheet(title=sheet_name)
        
        # 提取主表格（第二个表格，索引1）
        main_table = result['TableDetections'][1]
        cells = main_table['Cells']
        
        # 设置列宽
        column_widths = [8, 25, 8, 40, 10, 10, 10, 10, 10]
        for i, width in enumerate(column_widths, 1):
            col_letter = openpyxl.utils.get_column_letter(i)
            ws.column_dimensions[col_letter].width = width
        
        # 处理单元格
        merged_cells = []
        max_row = 0
        max_col = 0
        
        for cell in cells:
            row_start = cell['RowTl'] + 1
            row_end = cell['RowBr']
            col_start = cell['ColTl'] + 1
            col_end = cell['ColBr']
            
            # 更新最大行列
            max_row = max(max_row, row_end)
            max_col = max(max_col, col_end)
            
            # 处理文本：删除所有换行符
            text = cell['Text'].replace('\n', '')
            
            # 填充左上角单元格
            ws.cell(row=row_start, column=col_start, value=text)
            
            # 记录需要合并的单元格
            if row_end > row_start or col_end > col_start:
                merged_cells.append((row_start, row_end, col_start, col_end))
        
        # 应用合并单元格
        for merge in merged_cells:
            row_start, row_end, col_start, col_end = merge
            ws.merge_cells(
                start_row=row_start, end_row=row_end,
                start_column=col_start, end_column=col_end
            )
        
        # 设置居中和自动换行
        for row in ws.iter_rows(min_row=1, max_row=max_row, min_col=1, max_col=max_col):
            for cell in row:
                cell.alignment = Alignment(
                    horizontal='center', 
                    vertical='center',
                    wrap_text=True
                )
        
        return True
    
    except Exception as e:
        print(f"生成工作表时出错: {str(e)}")
        return False

# 主处理函数
def process_images(image_dir, output_excel):
    # 初始化OCR客户端
    ocr_client = init_ocr_client()
    
    # 获取图片列表
    image_files = []
    for file in os.listdir(image_dir):
        if file.lower().endswith(('.png', '.jpg', '.jpeg', '.bmp')):
            image_files.append(os.path.join(image_dir, file))
    
    if not image_files:
        print(f"在目录 {image_dir} 中未找到图片文件")
        return
    
    print(f"找到 {len(image_files)} 张图片需要处理")
    
    # 创建Excel工作簿
    wb = openpyxl.Workbook()
    # 删除默认创建的工作表
    if 'Sheet' in wb.sheetnames:
        del wb['Sheet']
    
    # 处理每张图片（使用多线程）
    success_count = 0
    sheet_names = []  # 记录已使用的工作表名称
    results = {}  # 存储结果：{图片路径: (文件名, sheet_name, 识别结果)}
    
    # 第一步：批量提交所有OCR请求
    print("开始批量提交OCR请求...")
    start_time = time.time()
    
    with ThreadPoolExecutor(max_workers=2) as executor:
        future_to_path = {
            executor.submit(recognize_table, image_path, ocr_client): image_path
            for image_path in image_files
        }
        
        for future in as_completed(future_to_path):
            image_path = future_to_path[future]
            filename = os.path.basename(image_path)
            base_name = os.path.splitext(filename)[0]
            
            try:
                result = future.result()
                # 创建安全的工作表名称
                sheet_name = create_safe_sheet_name(base_name, sheet_names)
                sheet_names.append(sheet_name)
                
                results[image_path] = (filename, sheet_name, result)
                print(f"  OCR完成: {filename}")
            except Exception as e:
                print(f"  处理图片 {filename} 时出错: {str(e)}")
    
    ocr_time = time.time() - start_time
    print(f"所有OCR请求完成! 总耗时: {ocr_time:.2f}s")
    
    # 第二步：处理结果并生成Excel工作表
    print("\n开始生成Excel工作表...")
    excel_start = time.time()
    
    for i, image_path in enumerate(image_files):
        if image_path not in results:
            continue
            
        filename, sheet_name, result = results[image_path]
        print(f"正在处理 ({i+1}/{len(image_files)}): {filename} -> 工作表: {sheet_name}")
        
        if result:
            success = ocr_to_excel_sheet(wb, result, sheet_name)
            if success:
                success_count += 1
                print(f"  工作表生成成功!")
            else:
                print(f"  生成工作表失败")
        else:
            print(f"  OCR识别失败")
    
    excel_time = time.time() - excel_start
    print(f"所有工作表生成完成! 耗时: {excel_time:.2f}s")
    
    # 保存Excel文件
    wb.save(output_excel)
    print(f"\n处理完成! 成功处理 {success_count}/{len(image_files)} 张图片")
    print(f"结果已保存到: {output_excel}")

# 启动批量处理
if __name__ == "__main__":
    process_images(IMAGE_DIR, OUTPUT_EXCEL)

简约版到此为至，进阶版有时再折腾。