Baidu AI Cloud Public Sector China Translated from Chinese

Huizhengtong

Document Processing

Huizhengtong needed to digitize large volumes of citizen and enterprise certificates for government-approval workflows while preserving accuracy, speed and data confidentiality.

Reported impact

Business outcome

Huizhengtong reduced manual data-entry effort in government approvals and created a privacy-conscious path from paper certificates to electronic records.

  • Compliance
  • Productivity Improvement
  • Risk Reduction

Solution

Huizhengtong integrated Baidu's iOCR custom-template recognition into its electronic certificate-approval system to replace heavy manual entry in government workflows. The implementation centers on private deployment: Baidu provides the iOCR model package, Huizhengtong deploys and operates the package locally, and then the company performs secondary development to wrap it into an electronic-certificate processing system for government use. Within that environment, administrators can define custom templates for many certificate layouts, select the fields that matter and use boxed regions to control extraction. When a document arrives, the system applies the corresponding template, extracts text and image information and presents an electronic-certificate draft that only needs human review or correction before seal and approval processes continue. The result is not just OCR as a feature, but a governed certificate-processing pipeline built for privacy-sensitive public-sector approval work.

Data flow

Paper certificates are uploaded into the approval system, matched with the right iOCR template, converted into structured fields and electronic certificate drafts, and then passed to human review and subsequent seal-processing steps.

Solution architecture

4 components · 3 layers
  1. Compute
    • OCR Provides the core text-recognition capability used across the approval workflow.
    • iOCR General Edition Acts as the Baidu OCR product foundation for Huizhengtong's certificate-processing system.
  2. Serving
    • Custom Template Recognition Lets operators define layouts and fields for many different certificate formats.
  3. Governance
    • Private Deployment Package Keeps OCR processing inside a locally controlled environment for sensitive certificate data.

Architecture clues

  • Field-level template definition is part of the operating workflow
  • Human review remains in the loop before seal processing
  • The OCR package is deployed privately and then wrapped into Huizhengtong's system through secondary development

Evidence from the source

After extraction, staff only need to review or modify the draft before continuing the seal process (original: 在生成电子证照后人工只需要审核/修改,即可轻松实现后续签章业务的办理).
Baidu supplies the iOCR model while Huizhengtong deploys the private package and performs secondary development (original: 百度大脑提供 iOCR 模型能力;慧政通部署、运行私有化部署包并二次开发为电子证照处理系统).
Huizhengtong's electronic-certificate approval system uses Baidu iOCR custom-template text recognition to extract document information automatically (original: 福建慧政通研发的电子证照审批系统在接入百度大脑 iOCR 自定义模板文字识别后,可实现证照模板的自定义配置,并能自动提取证件上的图文信息).
Transparency Evidence & methodology 75/100 reference value · 82% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value75/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact0/25
    0 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence82%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence15/20
    3 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity8/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Baidu AI Cloud

    Huizhengtong customer story

    First-party
    https://cloud.baidu.com/customer/case/huizheng.html
    Source type
    Vendor-published customer story
    Published
    May 15, 2025
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page May 15, 2025 · Baidu AI Cloud · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.