在上一篇文章中，我们成功串联了文本检测、方向分类、文本识别模型，搭建了一个基础的OCR文本识别服务。然而，针对复杂的PDF文档，仅靠基础的OCR功能还远远不够。为了实现全面的PDF文档解析，尤其是对于表格和版面内容的识别，还需要串联更多的目标检测模型，如表格识别和版面分析模型。
本篇文章将基于前两篇的内容，深入讲解如何将文本、表格、版面三个模型进行串联部署。通过对这些模型的串联，不仅可以处理文本，还能解析复杂的表格和多种布局格式的文档，使OCR系统具有商用水平。
在实践中，串联的文档解析系统能够处理各种高难度场景，如发票、合同、金融报表等包含复杂结构的文档。不仅支持传统的文本识别，还能够识别数学公式、表格、图表等内容，并且可以将解析结果输出为JSON、Markdown、HTML等多种格式，适应不同应用场景。
通过这些模型的串联和部署，用户可以构建一个完整的OCR文档解析服务，并支持企业自定义的模型训练和部署，进一步拓展为云平台API服务。这意味着企业可以根据自身需求定制模型并将其发布为服务，供用户调用，提升整体业务效率和文档处理能力。
Windows 环境下安装模型运行环境以及发布OCR文本识别服务的具体步骤和说明，请参考之前的文章，链接如下：
| Windows下OCR模型运行环境安装 |
| Windows安装PaddleHub |
PaddleOCR通过PaddleHub Serving发布串联的文本、表格、版面识别模型，利用structure_system模块实现文档解析的整体流程。具体过程如下：
工作原理：
文本检测模型：识别图像中的文字块位置，输出对应的检测框。
方向分类模型：矫正文字检测框的方向，确保字符的正确方向排列。
文本识别模型：从矫正后的框中提取文字，并输出识别结果。
表格识别：对于表格内容，模型会分析表格的结构和单元格，提取出相应的文本和位置关系。
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模型对于每个word都会预测出一个标签，相同标签的word聚合成一个实体（如titles、text、header等），而实体的外接框为实体中所有word的外接框。
可视化结果如下（不同颜色表示不同实体，如红色表示标题，青绿表示表格，蓝色表示页眉，绿色表示脚注等）：
deploy/hubserving/└─ocr_cls文本方向分类模块服务包└─ocr_det文本检测模块服务包└─ocr_rec文本识别模块服务包└─ocr_system文本检测+文本方向分类+文本识别串联服务包└─structure_layout版面分析服务包└─structure_table表格识别服务包└─structure_systemPP-Structure服务包└─kie_ser关键信息抽取-SER服务包└─kie_ser_re关键信息抽取-SER+RE服务包
deploy/hubserving/structure_system/└─__init__.py空文件，必选└─config.json配置文件，可选，使用配置启动服务时作为参数传入└─module.py主模块，必选，包含服务的完整逻辑└─params.py参数文件，必选，包含模型路径、前后处理参数等参数
| 模型 | 路径 |
| 检测模型 | ./inference/ch_PP-OCRv4_det_infer/ |
| 识别模型 | ./inference/ch_PP-OCRv4_rec_infer/ |
| 方向分类器 | ./inference/ch_ppocr_mobile_v2.0_cls_infer/ |
| 表格识别模型 | ./inference/ch_ppstructure_mobile_v2.0_SLANet_infer/ |
| 版面分析模型 | ./inference/picodet_lcnet_x1_0_fgd_layout_cdla_infer/ |
模型路径可在params.py中查看和修改，可以替换成自己训练转换好的模型。
/替换为\）下，安装模块命令如下表：| 服务模块 | 命令 |
| 全服务串联 | hub install deploy/hubserving/structure_system |
#执行全服务模型安装命令hub install deploy/hubserving/structure_system#执行后可以看到以下输出，说名全服务串联模型安装成功[2024-10-10 21:56:43,853] [INFO] - Successfully uninstalled structure_system[2024-10-1021:56:44,570][INFO]-Successfullyinstalledstructure_system-1.0.0
3. 启动服务
hubservingstart--modulesModule1==Version1,Module2==Version2,...\--port8866\--use_multiprocess\--workers\
--modules/-m | PaddleHub Serving预安装模型，以多个Module==Version键值对的形式列出 当不指定Version时，默认选择最新版本 |
--port/-p | 服务端口，默认为8866 |
| --use_multiprocess | 是否启用并发方式，默认为单进程方式，推荐多核CPU机器使用此方式 Windows操作系统只支持单进程方式 |
| --workers | 在并发方式下指定的并发任务数，默认为2*cpu_count-1，其中cpu_count为CPU核数 |
#执行后会出现以下图片输出，说明服务已经启动hubservingstart-mstructure_system
4. 请求服务
进入E:\paddle_ocr\PaddleOCR源码路径下，在praddle_env的Python的环境下，执行以下命令，验证已启动服务是否可用。
python./tools\test_hubserving.py--server_url=http://127.0.0.1:8866/predict/structure_system--image_dir=./doc\imgs/train_1430.jpg--visualize=true
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r><td>袁义祥</td><td>和升实业有限公司</td><td>董事</td><td>2017-06</td><td></td></tr><tr><td>袁义祥</td><td>润新微电子（大连）有限公司</td><td>监事会主席</td><td>2022-05</td><td></td></tr><tr><td>袁义祥</td><td>辽宁省机械（集团）股份有限公司</td><td>董事</td><td>2012-08</td><td></td></tr><tr><td>袁义祥</td><td>大连汇宇鑫科技有限公司</td><td>董事</td><td>2006-07</td><td></td></tr><tr><td>袁义祥</td><td>大连德润电动汽车科技有限公司</td><td>副董事长</td><td>2017-03</td><td></td></tr><tr><td>袁义祥</td><td>爱康普科技（大连）有限公司</td><td>董事</td><td>2016-04</td><td></td></tr><tr><td>袁义祥</td><td>大连业通投资有限公司</td><td>监事</td><td>2019-04</td><td></td></tr><tr><td>袁义祥</td><td>大连亿阳信息技术有限公司</td><td>执行董事、经理</td><td>2022-05</td><td></td></tr><tr><td>王文锋</td><td>大连和升控股集团有限公司</td><td>董事长、总经理</td><td>2007-07</td><td></td></tr><tr><td>王文锋</td><td>大连辽机路航特种车制造有限公司</td><td>董事长</td><td>2010-01</td><td></td></tr><tr><td>王文锋</td><td>大连三鑫投资有限公司</td><td>执行董事、经理</td><td>2010-01</td><td></td></tr><tr><td>王文锋</td><td>大连百傲化学股份有限公司</td><td>副董事长</td><td>2015-06</td><td></td></tr><tr><td>王文锋</td><td>和升实业有限公司</td><td>董事长、经理</td><td>2017-06</td><td></td></tr><tr><td>王文锋</td><td>北京和升创展食品发展有限责任公司</td><td>董事长、经理</td><td>2019-05</td><td></td></tr><tr><td>王文锋</td><td>大连听心会议服务有限公司</td><td>执行董事、经理</td><td>2020-09</td><td></td></tr><tr><td>王文锋</td><td>大连汇宇鑫科技有限公司</td><td>董事</td><td>2017-07</td><td></td></tr><tr><td>韩东丰</td><td>大连和升控股集团有限公司</td><td>董事、经理</td><td>2018-03</td><td></td></tr><tr><td>韩东丰</td><td>大连汇宇鑫科技有限公司</td><td>董事</td><td>2017-07</td><td></td></tr><tr><td>韩东丰</td><td>南京兰埔成新材料有限公司</td><td>董事</td><td>2016-04</td><td></td></tr><tr><td>韩东丰</td><td>大连万怡投资有限公司</td><td>执行董事、经理</td><td>2014-10</td><td></td></tr><tr><td>韩东丰</td><td>新大洲控股股份有限公司</td><td>董事长</td><td>2020-09-18</td><td></td></tr><tr><td>韩东丰</td><td>北京和升创展食品发展有限责任公司</td><td>董事</td><td>2019-05</td><td></td></tr><tr><td>韩东丰</td><td>沈阳和怡新材料有限公司</td><td>董事</td><td>2021-09</td><td></td></tr><tr><td>韩东丰</td><td>大连四达高技术发展有限公司</td><td>董事</td><td>2019-07</td><td></td></tr><tr><td>韩东丰</td><td>大连联创智达信息技术有限公司</td><td>董事</td><td>2019-10</td><td></td></tr><tr><td>韩东丰</td><td>盛银消费金融有限公司</td><td>董事</td><td>2019-08</td><td></td></tr></tbody></table></body></html>'},'score':0.977480947971344,'type':'table'},{'bbox':[161,225,1085,507],'img_idx':0,'res':{'cell_bbox':[[5.785220623016357,24.311243057250977,181.07508850097656,23.637800216674805,185.1851348876953,72.7176742553711,5.823643684387207,73.16725158691406],[178.80596923828125,22.35544204711914,419.8334655761719,21.064998626708984,425.7052307128906,63.92222213745117,180.6250762939453,66.30516815185547],[400.674072265625,9.518608093261719,606.553466796875,9.219185829162598,610.5952758789062,69.24838256835938,403.7786560058594,72.22607421875],[581.6190795898438,18.830272674560547,768.76611328125,18.454299926757812,769.8507690429688,67.78468322753906,584.0712890625,69.25111389160156],[748.94189453125,19.57159423828125,914.4827880859375,19.501468658447266,914.4170532226562,72.4549560546875,750.02880859375,72.8739242553711],[6.469827651977539,67.82855224609375,165.38909912109375,67.83541107177734,165.43399047851562,103.52085876464844,6.291061878204346,103.28355407714844],[173.060546875,67.76287078857422,422.35882568359375,67.77702331542969,421.85748291015625,99.12926483154297,170.94956970214844,99.13125610351562],[416.9671936035156,62.83456039428711,587.1475830078125,63.146575927734375,585.9091796875,100.19012451171875,414.5832824707031,99.99385833740234],[592.1345825195312,64.03622436523438,750.5368041992188,63.95559310913086,749.17626953125,97.11198425292969,589.9861450195312,97.40837860107422],[780.7509155273438,63.151573181152344,911.5607299804688,63.30153274536133,911.3961181640625,97.13643646240234,779.7352294921875,97.09942626953125],[6.2628092765808105,99.06214904785156,170.69480895996094,99.63970947265625,170.6552276611328,129.71847534179688,6.143228054046631,129.2323760986328],[173.407958984375,98.6748046875,420.6581115722656,98.76355743408203,419.62921142578125,127.04004669189453,171.16477966308594,127.03429412841797],[416.32757568359375,96.16886901855469,584.0556640625,96.40225219726562,582.3076171875,124.58045959472656,413.4111633300781,124.51908874511719],[590.5037231445312,96.67974090576172,752.9931030273438,96.6097640991211,751.8983764648438,124.3765640258789,588.7061157226562,124.61097717285156],[785.4635620117188,96.46088409423828,912.8157348632812,96.69051361083984,912.662841796875,126.30024719238281,784.5137329101562,126.12730407714844],[5.720996856689453,126.72210693359375,172.47499084472656,127.42425537109375,172.48741149902344,158.52371215820312,5.623476982116699,157.99790954589844],[174.05043029785156,126.45726013183594,418.9090576171875,127.03121185302734,417.4536437988281,156.33580017089844,171.69680786132812,155.88070678710938],[418.59783935546875,124.23936462402344,587.9127197265625,124.83297729492188,585.6192016601562,154.0175323486328,414.9079284667969,153.5950469970703],[593.3422241210938,124.64849090576172,757.4677734375,124.85608673095703,756.1747436523438,153.73577880859375,591.0472412109375,153.64122009277344],[786.30987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字段名称 | 数据类型 | 意义 |
| text | str | 文本内容 |
| confidence | float | 文本识别置信度或角度分类置信度 |
| text_region | list | 文本位置坐标 |
| html | str | 表格的html字符 |
| regions | list | 版面分析+表格识别+OCR的结果，每一项为一个list 包含表示区域坐标的 bbox，区域类型的type和区域结果的res三个字段 |
说明：如果需要增加、删除、修改返回字段，可在相应模块的module.py文件中进行修改。
总结
当文本检测、方向分类、文本识别、表格识别和版面识别这几个模型成功串联并发布后，整个系统便能够完整解析PDF、图片等文档中的内容。百度提供的通用OCR模型已经能够满足大部分业务场景中对PDF和图片数据的解析需求，返回结果为JSON格式，其中表格内容则以HTML形式展示。该服务具有基本的商用功能，能够承接文档解析业务。如果再结合APP应用层，该系统可以灵活适应更多业务场景，扩展其应用范围。
此外，通过发布SER（结构化实体识别）模型，系统还能够实现对发票、证件等特殊文档的关键信息抽取，提供更强大的文档解析功能。而该系统架构的最大优势在于支持自定义模型的发布，用户能够基于行业需求对模型进行精调和微调，结合实际业务场景标注数据、训练模型并实现部署，从而大大提升行业专用模型的性能与适配度。
整体来说，这样的架构不仅满足了通用文档解析的需求，还为用户提供了更高的灵活性和可扩展性，使其能够轻松应对复杂多变的业务场景。
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