Baidu AI Cloud Public Sector China Translated from Chinese

Chongqing High People's Court

Document Processing · Enterprise Knowledge Search

Chongqing's courts needed to reduce the repetitive workload of clerks and judges, especially in transcription, evidence review, case lookup, and document drafting, while improving the speed and consistency of judicial handling.

Clerk workload was reduced 70%.
Similar-case matching efficiency improved 80%.
Speech-recognition accuracy exceeded 95%.

Reported impact

Business outcome

Chongqing courts reduced clerical repetition, accelerated similar-case research and document drafting, and gave judicial staff a more efficient digital-assistance layer for case handling.

  • Compliance
  • Customer Experience
  • Productivity Improvement

Solution

Chongqing High People's Court and Baidu AI Cloud built the Easy Trial platform using OCR, NLP, and real-time speech recognition to support multiple judicial workflows. Uploaded litigation materials are classified automatically and key fields are filled back into case records. During hearings, speech recognition converts courtroom audio into text so clerks are not tied to manual transcription and appellate judges can locate important remarks quickly in long recordings. The same AI layer also powers similar-case recommendation and intelligent document generation, giving judges precedent-style references, legal knowledge graphs, and drafting support throughout the case lifecycle. The platform sits inside the court's broader '4+1' smart-court ecosystem, where Easy Trial is one of four major platforms and the 'Law Intelligence Cloud Center' provides shared intelligence support.

Data flow

Litigation documents and hearing audio enter the Easy Trial platform, where OCR and speech recognition convert them into structured text. NLP services then route the information into case files, similar-case recommendations, legal knowledge views, and document-generation workflows that support judges and clerks.

Solution architecture

4 components · 3 layers
  1. Ingest
    • OCR Text Recognition Classifies litigation materials and extracts case information for automatic filing and field completion.
    • Real-Time Speech Recognition Transcribes courtroom speech to reduce manual clerk workload and speed judicial review.
  2. Compute
    • Language Understanding Technology Supports legal text understanding, similar-case recommendation, and document-generation workflows.
  3. Serving
    • Easy Trial Acts as the intelligent case-handling platform used by judges and court staff.

Architecture clues

  • Courtroom transcription is used to help both clerks and appellate judges review hearing content faster
  • The platform is part of a broader '4+1' smart-court ecosystem
  • The same AI stack supports automatic filing, field completion, similar-case recommendation, and document generation

Evidence from the source

The Easy Trial platform uses Baidu AI Cloud's OCR, NLP, and speech-recognition technologies to automate material classification, field completion, similar-case recommendation, and document generation (original: 通过使用百度智能云OCR、NLP、语音识别等自主可控的AI技术,实现了诉讼材料上传自动归目、信息智能回填、类案智能推送、文书智能生成等众多功能).
The court built a '4+1 Chongqing smart-court new ecosystem' with Easy Trial as one of four platforms and the Law Intelligence Cloud Center as the shared hub (original: 核心架构就是“4+1”,“4”即“易审、易诉、易解、易达4平台”,“1”即“法智云中心”).
The courtroom speech-recognition system frees clerks from manual note-taking and helps appellate judges locate the key points of a hearing quickly (original: 系统可以直接将庭审语音转文字…让书记员从记录工作中彻底解放…二审法官不必再从冗长的庭审录音录像中寻找案件的重点).
The similar-case system pushes prior reasoning and outcomes and can provide a panoramic legal knowledge graph for judges (original: 系统可以为法官推送相似案件的既往裁判思路和结果…系统可推送全景式的法律知识图谱).
Transparency Evidence & methodology 95/100 reference value · 89% 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 value95/100
  • 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 impact25/25
    3 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • 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 evidence20/20
    4 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 specificity10/10
    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

    Chongqing High People's Court customer story

    First-party
    https://cloud.baidu.com/customer/case/chongqing.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.