Exam App Enhancements: OCR, Markdown & OpenAI GPT
Budget: $750 – $1,500 AUD
OCR, Markdown Rendering & GPT Context Integration for Exam Generation App
Project Overview:
I have an Exam Generation App built in Bolt.new (using Tailwind CSS). I need a specialist to implement three key features:
OCR (Optical Character Recognition) for uploaded PDFs (or images of past exams and marking guides).
Markdown support (reading and rendering teacher-edited text, “MarkItDown” style).
Context Understanding via GPT (OpenAI or similar LLM) to categorize extracted data and suggest relevant exam questions by semester/topic.
Scope of Work:
OCR Implementation
Set up OCR using Tesseract.js (client-side) or an external OCR API.
Convert multi-page PDFs into text. If using Tesseract.js, handle PDF->Image conversion (e.g., using pdf.js or a server-side function).
Store extracted text (alongside metadata like page number, exam ID) in Firebase Firestore (which will be already configured or in progress).
Markdown (MarkItDown) Integration
Integrate a JavaScript Markdown library (e.g., Marked.js or Showdown) so teachers can view and edit OCR text.
Create a simple editor/preview interface in Bolt.new for easy text editing.
Ensure the final output is styled with Tailwind (using “prose” classes, etc.).
Context Understanding with GPT
Implement an API call to GPT (e.g., OpenAI’s text-davinci or gpt-3.5/4) so that the extracted text can be automatically summarized or categorized by semester, subject, or specific topics.
Optionally, set up a prompt to “suggest new exam questions” based on the user’s text input and the stored OCR data.
Show how to store GPT’s responses (summaries or embeddings) in Firestore.
Documentation & Implementation Guidance
Provide detailed instructions on how each piece fits together (OCR -> Markdown -> GPT).
Write sample code or a mini tutorial for a novice coder: “Paste this code in Bolt.new to perform OCR on upload,” etc.
Share best practices for scaling or improving accuracy (e.g., potential transition to server-side OCR, or better GPT prompt engineering).
Required Experience & Skills:
Strong JavaScript background with OCR libraries like Tesseract.js or pdf.js.
Familiarity with Markdown libraries (Marked, Showdown, or TipTap).
Experience integrating Large Language Models (OpenAI, GPT-3.5 or GPT-4) into a web project.
Comfortable writing thorough documentation aimed at a novice coder.
Deliverables:
Functional OCR pipeline within the existing Bolt.new front end or a server-side alternative.
A Markdown editor + preview component integrated into the UI, styled with Tailwind.
GPT-based context or question suggestions that reference the OCR’d text.
Clear, step-by-step instructions for maintenance and future enhancements.
Budget & Timeline:
Budget: $750 AUD
Timeline: 1-2 Week - Given Complexity
Payment terms: Milestones upon partial completion (OCR done), final upon GPT integration + handover.
Project Overview:
I have an Exam Generation App built in Bolt.new (using Tailwind CSS). I need a specialist to implement three key features:
OCR (Optical Character Recognition) for uploaded PDFs (or images of past exams and marking guides).
Markdown support (reading and rendering teacher-edited text, “MarkItDown” style).
Context Understanding via GPT (OpenAI or similar LLM) to categorize extracted data and suggest relevant exam questions by semester/topic.
Scope of Work:
OCR Implementation
Set up OCR using Tesseract.js (client-side) or an external OCR API.
Convert multi-page PDFs into text. If using Tesseract.js, handle PDF->Image conversion (e.g., using pdf.js or a server-side function).
Store extracted text (alongside metadata like page number, exam ID) in Firebase Firestore (which will be already configured or in progress).
Markdown (MarkItDown) Integration
Integrate a JavaScript Markdown library (e.g., Marked.js or Showdown) so teachers can view and edit OCR text.
Create a simple editor/preview interface in Bolt.new for easy text editing.
Ensure the final output is styled with Tailwind (using “prose” classes, etc.).
Context Understanding with GPT
Implement an API call to GPT (e.g., OpenAI’s text-davinci or gpt-3.5/4) so that the extracted text can be automatically summarized or categorized by semester, subject, or specific topics.
Optionally, set up a prompt to “suggest new exam questions” based on the user’s text input and the stored OCR data.
Show how to store GPT’s responses (summaries or embeddings) in Firestore.
Documentation & Implementation Guidance
Provide detailed instructions on how each piece fits together (OCR -> Markdown -> GPT).
Write sample code or a mini tutorial for a novice coder: “Paste this code in Bolt.new to perform OCR on upload,” etc.
Share best practices for scaling or improving accuracy (e.g., potential transition to server-side OCR, or better GPT prompt engineering).
Required Experience & Skills:
Strong JavaScript background with OCR libraries like Tesseract.js or pdf.js.
Familiarity with Markdown libraries (Marked, Showdown, or TipTap).
Experience integrating Large Language Models (OpenAI, GPT-3.5 or GPT-4) into a web project.
Comfortable writing thorough documentation aimed at a novice coder.
Deliverables:
Functional OCR pipeline within the existing Bolt.new front end or a server-side alternative.
A Markdown editor + preview component integrated into the UI, styled with Tailwind.
GPT-based context or question suggestions that reference the OCR’d text.
Clear, step-by-step instructions for maintenance and future enhancements.
Budget & Timeline:
Budget: $750 AUD
Timeline: 1-2 Week - Given Complexity
Payment terms: Milestones upon partial completion (OCR done), final upon GPT integration + handover.