✅ What problem does this workflow solve?
✅ What problem does this workflow solve? Manual checking of OMR (Optical Mark Recognition) answer sheets is time-consuming, error-prone, and difficult to scale—especially for schools, coaching institutes, and exam centers. This workflow automates OMR evaluation end-to-end using AI, from reading a scanned answer sheet image to calculating scores and storing structured results in Google Sheets. ⚙️ What does this workflow do? Accepts a scanned OMR answer sheet image via webhook. Uses AI vision to extract only the marked answers from the sheet. Extracts basic student details (Name, Roll Number, Class). Compares extracted answers with a predefined answer key. Calculates: Total questions Correct answers Incorrect answers Score percentage Generates question-wise binary results (1 = correct, 0 = incorrect). Stores the complete result in Google Sheets. Returns a structured JSON response to the calling system. 🧠 How It Works – Step by Step 1. 📥 Webhook Trigger (Student OMR Upload) A client uploads the OMR image via a POST request. Image is received as form-data (key: file). 2. 👁️ AI-Based OMR Image Analysis An AI vision model analyzes the image. Strict rules ensure: Only answer bubbles are considered Multiple markings → darkest option is selected Unmarked questions are skipped No guessing or hallucination Output includes: Student details Question–answer pairs 3. 🔄 Answer Formatting Raw AI output is converted into a clean, structured format: 1:A, 2:B, 3:C, ... Student metadata is preserved separately. 4. 🧮 Answer Key Setup Correct answers are defined inside the workflow (editable anytime). Supports any number of questions. 5. 📊 Result Calculation User answers are compared with the answer key. Generates: Correct / Incorrect counts Percentage score Detailed per-question result Binary output (Q.1 = 1 / 0) for analytics 6. 📄 Google Sheets Logging Results are appended to a Google Sheet with columns such as: Student Name Roll No Class Correct Incorrect Score Percentage Q.1 → Q.n (binary values) 7. 📤 API Response Workflow responds with a JSON payload containing: Student details Full evaluation summary Per-question analysis 📂 Sample Google Sheet Output | Student Name | Roll No | Class | Correct | Incorrect | Score % | Q.1 | Q.2 | Q.3 | ... | |-------------|--------|-------|---------|-----------|---------|-----|-----|-----|-----| | Rahul Shah | 1023 | 10-A | 16 | 4 | 80% | 1 | 0 | 1 | ... | 🛠 Integrations Used 🤖 AI Vision Model – for accurate OMR detection ⚙️ n8n Webhook – to accept image uploads 🧠 Custom Code Nodes – for parsing and evaluation logic 📊 Google Sheets – for persistent result storage 👤 Who can use this? This workflow is ideal for: 🏫 Schools & Colleges 📚 Coaching Institutes 🧪 Online Exam Platforms 🧑💻 EdTech Developers 📝 Mock Test Providers If you need fast, reliable, and scalable OMR checking without expensive hardware—this workflow delivers. 🚀 Benefits ⏱ Saves hours of manual checking 🎯 Eliminates human error 📊 Produces analytics-ready data 🔄 Easy to update answer keys 🌐 API-ready for integration with any system 📦 Ready to Deploy? Just configure: ✅ AI model credentials ✅ Google Sheets access ✅ Your correct answer key …and start evaluating OMR sheets automatically at scale. An n8n automation workflow template by InfyOm Technologies.
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