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GP-CS-0SW6OFXComputer ScienceOpen for request

Accessible examination content converter

A document-processing system that converts examination material into structured accessible text, navigable HTML, and reviewable audio output.

  • Nuxt
  • Vue
  • FastAPI
  • Python
  • OCR
  • MathML
  • SSML
  • Docker

Project definition

Problem statement

Examination material may contain scanned pages, columns, tables, mathematical notation, diagrams, instructions, and marks. Simple text extraction can lose reading order and structure, making the result difficult to navigate with assistive technology.

The engineering problem is to detect document structure, preserve question numbering and mathematical content, generate accessible output formats, and provide a review stage for elements that cannot be converted reliably.

Project objectives

  • Extract text and page structure from digital and scanned examination documents.
  • Preserve reading order, sections, question numbers, marks, lists, tables, and mathematical notation.
  • Generate accessible structured text, HTML, and audio-ready output.
  • Flag uncertain OCR, diagrams, and complex layout for manual review.
  • Measure extraction accuracy, structure preservation, accessibility checks, and review effort.

System design

System modules

01

Document ingestion

Accepts supported document types, separates pages, records document metadata, and identifies digital or scanned content.

02

OCR and layout analysis

Extracts text, identifies regions, estimates reading order, and records confidence for each detected element.

03

Structure parser

Identifies headings, instructions, sections, questions, subquestions, marks, tables, and mathematical regions.

04

Accessible conversion

Builds semantic text and HTML output, converts supported mathematics, and prepares structured content for speech.

05

Review interface

Shows source and converted content together and allows correction of order, text, labels, equations, and descriptions.

06

Validation subsystem

Runs structural, accessibility, OCR, navigation, and output consistency checks against prepared documents.

Methodology

System workflow

  1. 01
    Input examination document

    The system validates the file, separates pages, and identifies whether OCR is required.

  2. 02
    Extract content and layout

    Text, regions, coordinates, confidence values, and page images are produced.

  3. 03
    Build semantic structure

    The parser reconstructs instructions, question hierarchy, marks, tables, and supported mathematical content.

  4. 04
    Review uncertain elements

    Low-confidence text, diagrams, complex tables, and equations are presented for correction.

  5. 05
    Export and validate

    The corrected document is exported to supported formats and checked for navigation, structure, and content consistency.

Demonstration scenario

A scanned examination paper containing instructions, numbered questions, a table, and mathematical notation is processed. The system reconstructs its hierarchy, flags one equation and a diagram for review, accepts corrections, and exports navigable HTML, structured text, and audio-ready content.

Engineering

Technical architecture

Web application
Nuxt and Vue for document submission, side-by-side review, correction, preview, and export.
Processing API
FastAPI for file handling, conversion jobs, review state, validation results, and generated output.
Document pipeline
Python-based page rendering, OCR, layout-region detection, reading-order analysis, and semantic reconstruction.
Accessible formats
Semantic HTML, structured text, MathML where supported, and SSML-based preparation for audio generation.
Quality controls
Confidence thresholds, manual review queues, structural validation, and comparison with prepared reference transcripts.
Delivery
Container-based setup with sample examination documents, expected outputs, and repeatable conversion tests.

Testing

Evaluation

Evaluation measures

  • Character and word accuracy for digital and scanned input
  • Reading-order and question-hierarchy accuracy
  • Preservation of marks, tables, and supported mathematical notation
  • Automated accessibility and document-structure check results
  • Audio sequence consistency with the reviewed structured text
  • Manual correction time across the prepared document set

System boundaries

  • The system converts examination content and does not generate answers or solve questions.
  • Complex diagrams and uncertain mathematical notation require human review.
  • The prototype does not replace formal accommodation review by the responsible institution.
  • Copyright-protected examination material is used only with permission or replaced by prepared samples.

Included

  1. 01Document conversion and accessibility review interfaces
  2. 02OCR, layout analysis, structure extraction, and export pipeline
  3. 03Accessible HTML, structured text, and audio output formats
  4. 04Prepared examination documents and conversion test results
  5. 05Complete source code in a private GitHub repository
  6. 06Complete project documentation with synopsis, abstract, methodology, processing diagrams, accessibility checks, test results, screenshots, and conclusion

Project record

No buyer information is collected on this page.

Permanent project ID
GP-CS-0SW6OFX
Catalogued
21 Aug 2026
Completed
Pending
Verified
Pending
Demonstration
Added when ready

Handover

After purchase

  1. 01
    Payment is confirmed

    The project is marked unavailable and cannot be purchased again.

  2. 02
    Repository access is granted

    The buyer's submitted GitHub account receives access to the private repository.

  3. 03
    The purchase record is delivered

    The certification sheet is prepared from the reviewed buyer details and sent privately by email.