InfyOm Technologies This workflow corresponds to n8n.io template #12549 — we link there as the canonical source.
The workflow JSON
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{
"meta": {
"templateCredsSetupCompleted": true
},
"nodes": [
{
"id": "6fc105e9-70fb-4b39-94aa-443c6ce55dfe",
"name": "Analyze image",
"type": "@n8n/n8n-nodes-langchain.ollama",
"position": [
960,
1936
],
"parameters": {
"text": "=You are an OMR sheet analysis system.\n\nTask:\nAnalyze the provided OMR answer sheet image and extract ONLY the student's marked answers.\n\nStrict rules:\n- Ignore all headers, instructions, logos, barcodes, or any other text.\n- High Focus ONLY on the answer bubbles and properlly right answer provide.\n- For each question, detect the clearly filled/marked option (A, B, C, or D).\n- If multiple bubbles are filled for a question, select the darkest/most clearly marked one.\n- If no option is clearly marked for a question, skip that question entirely.\n- Do NOT guess answers.\n- Do NOT explain anything.\n- Do NOT add confidence scores or metadata.\n\nOutput format (MANDATORY):\n- Plain text only\n- Exactly in the format shown below\n- One answer per line\n- Sequential numbering starting from question 1\n- No extra spaces, symbols, or commentary\n- Extract a student details\n\nRequired output structure:\n \"student\":\n \"name\": \"string | null\",\n \"rollNumber\": \"string | null\",\n \"class\": \"string | null\"\nanswer :\n1. A\n2. B\n3. C\n4. D\n5. A\n...\n(continue until the last detected question)",
"modelId": {
"__rl": true,
"mode": "list",
"value": "gemini-3-flash-preview",
"cachedResultName": "gemini-3-flash-preview"
},
"options": {
"format": "json"
},
"resource": "image",
"simplify": false
},
"typeVersion": 1
},
{
"id": "e98c50f8-98e3-4b94-951c-40a94197cd6b",
"name": "Merge Answer",
"type": "n8n-nodes-base.merge",
"position": [
1408,
1888
],
"parameters": {
"mode": "combine",
"options": {},
"combineBy": "combineByPosition"
},
"typeVersion": 3.2
},
{
"id": "0d328c98-9fe6-487e-bbb1-cca7a93539dc",
"name": "Calculate Result",
"type": "n8n-nodes-base.code",
"position": [
1616,
1856
],
"parameters": {
"jsCode": "// // function parseAnswers(input) {\n// // if (!input) return {};\n\n// // // If already object, return as is\n// // if (typeof input === \"object\") {\n// // return input;\n// // }\n\n// // // If string, normalize and parse\n// // if (typeof input === \"string\") {\n// // let obj = {};\n// // let parts = input\n// // .replace(/->/g, \":\") // handle \"->\" also\n// // .replace(/\\n/g, \",\") // handle line breaks\n// // .split(\",\");\n\n// // for (let p of parts) {\n// // let [q, ans] = p.split(\":\").map(s => s.trim());\n// // if (q && ans) obj[q] = ans.toUpperCase();\n// // }\n// // return obj;\n// // }\n\n// // return {};\n// // }\n\n// // // ---- Extract Inputs (safe handling)\n// // const item = $input.item?.json || {};\n// // const originalRaw = item.chatInput || item.CorrectAnswer || {};\n// // const userRaw = item.UserAnswers || {};\n\n// // const original = parseAnswers(originalRaw);\n// // const user = parseAnswers(userRaw);\n\n// // let correct = 0;\n// // let incorrect = 0;\n// // let results = [];\n\n// // const total = Object.keys(original).length || Object.keys(user).length || 0;\n\n// // // ---- Comparison logic\n// // for (let i = 1; i <= total; i++) {\n// // const q = String(i);\n// // const correctAns = original[q] || \"Not Available\";\n// // const given = user[q] || \"Not Attempted\";\n\n// // if (given === correctAns) {\n// // correct++;\n// // results.push({ question: i, given, correctAns, status: \"Correct\" });\n// // } else {\n// // incorrect++;\n// // results.push({ question: i, given, correctAns, status: \"Incorrect\" });\n// // }\n// // }\n\n// // // ---- Calculate percentage safely\n// // const scorePercentage = total > 0 ? ((correct / total) * 100).toFixed(2) : \"0.00\";\n\n// // // ---- Return final structured output\n// // return [\n// // {\n// // json: {\n// // totalQuestions: total,\n// // correct,\n// // incorrect,\n// // scorePercentage,\n// // results\n// // }\n// // }\n// // ];\n\n\n\n\n// function parseAnswers(input) {\n// if (!input) return {};\n\n// // If already object, return as is\n// if (typeof input === \"object\") {\n// return input;\n// }\n\n// // If string, normalize and parse\n// if (typeof input === \"string\") {\n// let obj = {};\n// let parts = input\n// .replace(/->/g, \":\")\n// .replace(/\\n/g, \",\")\n// .split(\",\");\n\n// for (let p of parts) {\n// let [q, ans] = p.split(\":\").map(s => s.trim());\n// if (q && ans) obj[q] = ans.toUpperCase();\n// }\n// return obj;\n// }\n\n// return {};\n// }\n\n// // ---- Extract Inputs\n// const item = $input.item?.json || {};\n\n// // \u2705 STUDENT DETAILS (DIRECT, NO PARSING)\n// const student = item.student || {\n// name: null,\n// rollNumber: null,\n// class: null\n// };\n\n// // ---- Answers\n// const originalRaw = item.chatInput || item.CorrectAnswer || {};\n// const userRaw = item.UserAnswers || {};\n\n// const original = parseAnswers(originalRaw);\n// const user = parseAnswers(userRaw);\n\n// // ---- Evaluation\n// let correct = 0;\n// let incorrect = 0;\n// let results = [];\n\n// const total =\n// Object.keys(original).length ||\n// Object.keys(user).length ||\n// 0;\n\n// for (let i = 1; i <= total; i++) {\n// const q = String(i);\n// const correctAns = original[q] || \"Not Available\";\n// const given = user[q] || \"Not Attempted\";\n\n// if (given === correctAns) {\n// correct++;\n// results.push({\n// question: i,\n// given,\n// correctAns,\n// status: \"Correct\"\n// });\n// } else {\n// incorrect++;\n// results.push({\n// question: i,\n// given,\n// correctAns,\n// status: \"Incorrect\"\n// });\n// }\n// }\n\n// // ---- Score\n// const scorePercentage =\n// total > 0 ? ((correct / total) * 100).toFixed(2) : \"0.00\";\n\n// // ---- FINAL API RESPONSE\n// return [\n// {\n// json: {\n// student,\n// totalQuestions: total,\n// correct,\n// incorrect,\n// scorePercentage,\n// results\n// }\n// }\n// ];\n\n\nfunction parseAnswers(input) {\n if (!input) return {};\n\n if (typeof input === \"object\") {\n return input;\n }\n\n if (typeof input === \"string\") {\n let obj = {};\n let parts = input\n .replace(/->/g, \":\")\n .replace(/\\n/g, \",\")\n .split(\",\");\n\n for (let p of parts) {\n let [q, ans] = p.split(\":\").map(s => s.trim());\n if (q && ans) obj[q] = ans.toUpperCase();\n }\n return obj;\n }\n\n return {};\n}\n\n// ---- Extract Inputs\nconst item = $input.item?.json || {};\n\n// ---- Student Details\nconst student = item.student || {\n name: null,\n rollNumber: null,\n class: null\n};\n\n// ---- Answers\nconst original = parseAnswers(item.chatInput || item.CorrectAnswer || {});\nconst user = parseAnswers(item.UserAnswers || {});\n\n// ---- Evaluation\nlet correct = 0;\nlet incorrect = 0;\nlet results = [];\nlet binaryResult = {}; // \ud83d\udc48 for Google Sheets (Q.1 = 1/0)\n\nconst total =\n Object.keys(original).length ||\n Object.keys(user).length ||\n 0;\n\nfor (let i = 1; i <= total; i++) {\n const q = String(i);\n const correctAns = original[q] || \"Not Available\";\n const given = user[q] || \"Not Attempted\";\n\n const isCorrect = given === correctAns;\n const value = isCorrect ? 1 : 0;\n\n if (isCorrect) {\n correct++;\n } else {\n incorrect++;\n }\n\n results.push({\n question: i,\n given,\n correctAns,\n status: isCorrect ? \"Correct\" : \"Incorrect\",\n value // \ud83d\udc48 1 or 0\n });\n\n binaryResult[`Q.${i}`] = value; // \ud83d\udc48 Q.1, Q.2, ...\n}\n\n// ---- Score\nconst scorePercentage =\n total > 0 ? ((correct / total) * 100).toFixed(2) : \"0.00\";\n\n// ---- FINAL OUTPUT\nreturn [\n {\n json: {\n student,\n totalQuestions: total,\n correct,\n incorrect,\n scorePercentage,\n results, // detailed\n binaryResult // \ud83d\udc48 PERFECT FOR GOOGLE SHEETS\n }\n }\n];\n"
},
"typeVersion": 2
},
{
"id": "78f5bed1-1583-4429-ae5b-960be1d44795",
"name": "Send Result Respond",
"type": "n8n-nodes-base.respondToWebhook",
"position": [
2016,
1856
],
"parameters": {
"options": {},
"respondWith": "json",
"responseBody": "={{ {\n status: \"success\",\n data: $json\n} }}"
},
"typeVersion": 1.4
},
{
"id": "21acd37d-0f9b-4475-96a5-794a7497d4e4",
"name": "Convert User Ans in Formate",
"type": "n8n-nodes-base.code",
"position": [
1216,
1968
],
"parameters": {
"jsCode": "// // Get input JSON\n// const input = items[0].json;\n\n// // Get raw AI response text\n// const content = input.message?.content || \"\";\n\n// // Regex to capture answers like \"1:A\", \"2 : B\", etc.\n// const regex = /(\\d+)\\s*[:\\.]\\s*([A-D])/g;\n\n// let match;\n// const pairs = [];\n\n// // Extract question-answer pairs\n// while ((match = regex.exec(content)) !== null) {\n// pairs.push(`${match[1]}:${match[2]}`);\n// }\n\n// // Join into a single string\n// const userAnswersStr = pairs.join(\", \");\n\n// // Return ONLY user answers\n// return [\n// {\n// json: {\n// UserAnswers: userAnswersStr\n// }\n// }\n// ];\n\n\n\n// ===============================\n// 1. GET INPUT\n// ===============================\nconst input = items[0].json;\nlet content = input.message?.content || \"\";\n\n// Remove ``` fences if present\ncontent = content.replace(/```/g, \"\").trim();\n\n// ===============================\n// 2. EXTRACT STUDENT DETAILS\n// ===============================\nconst student = {\n name: null,\n rollNumber: null,\n class: null\n};\n\n// Name\nconst nameMatch = content.match(/\"name\"\\s*:\\s*\"([^\"]+)\"/i);\nif (nameMatch) student.name = nameMatch[1].trim();\n\n// Roll Number\nconst rollMatch = content.match(/\"rollNumber\"\\s*:\\s*\"([^\"]+)\"/i);\nif (rollMatch) student.rollNumber = rollMatch[1].trim();\n\n// Class\nconst classMatch = content.match(/\"class\"\\s*:\\s*\"([^\"]+)\"/i);\nif (classMatch) student.class = classMatch[1].trim();\n\n// ===============================\n// 3. EXTRACT USER ANSWERS\n// ===============================\n\n// Regex to capture answers like \"1:A\", \"1. A\", \"2 : B\"\nconst regex = /(\\d+)\\s*[:\\.]\\s*([A-D])/g;\n\nlet match;\nconst pairs = [];\n\n// Extract question-answer pairs\nwhile ((match = regex.exec(content)) !== null) {\n pairs.push(`${match[1]}:${match[2]}`);\n}\n\n// Join into a single string\nconst userAnswersStr = pairs.join(\", \");\n\n// ===============================\n// 4. FINAL OUTPUT\n// ===============================\nreturn [\n {\n json: {\n student,\n UserAnswers: userAnswersStr\n }\n }\n];\n"
},
"typeVersion": 2
},
{
"id": "d4f4b88d-30c3-4355-a404-b9000db1d2ea",
"name": "Set Your Correct Answer",
"type": "n8n-nodes-base.set",
"position": [
1216,
1824
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "1209b6e5-0167-4c2a-b9bd-b894760a4027",
"name": "CorrectAnswer",
"type": "string",
"value": "1:B, 2:A, 3:D, 4:C, 5:B, 6:D, 7:B, 8:C, 9:A, 10:B, 11:D, 12:C, 13:A, 14:D, 15:B, 16:C, 17:D, 18:A, 19:B, 20:C"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "0bd1adb7-3475-4251-b343-79b2608d800f",
"name": "Send Student Ans Img",
"type": "n8n-nodes-base.webhook",
"position": [
640,
1856
],
"parameters": {
"path": "omr-sheet-checker",
"options": {},
"httpMethod": "POST"
},
"typeVersion": 2.1
},
{
"id": "52442ffc-824d-432b-aaad-80eab9257104",
"name": "Append Result in Sheet",
"type": "n8n-nodes-base.googleSheets",
"position": [
1824,
1856
],
"parameters": {
"columns": {
"value": {
"Q.1": "={{ $json.results[0].value }}",
"Q.2": "={{ $json.results[1].value }}",
"Q.3": "={{ $json.results[2].value }}",
"Q.4": "={{ $json.results[3].value }}",
"Q.5": "={{ $json.results[4].value }}",
"Q.6": "={{ $json.results[5].value }}",
"Q.7": "={{ $json.results[6].value }}",
"Q.8": "={{ $json.results[7].value }}",
"Q.9": "={{ $json.results[8].value }}",
"Q.10": "={{ $json.results[9].value }}",
"Q.11": "={{ $json.results[10].value }}",
"Q.12": "={{ $json.results[11].value }}",
"Q.13": "={{ $json.results[12].value }}",
"Q.14": "={{ $json.results[13].value }}",
"Q.15": "={{ $json.results[14].value }}",
"Q.16": "={{ $json.results[15].value }}",
"Q.17": "={{ $json.results[16].value }}",
"Q.18": "={{ $json.results[17].value }}",
"Q.19": "={{ $json.results[18].value }}",
"Q.20": "={{ $json.results[19].value }}",
"Class": "={{ $json.student.class }}",
"Correct": "={{ $json.correct }}",
"Roll No": "={{ $json.student.rollNumber }}",
"Incorrect": "={{ $json.incorrect }}",
"Student Name": "={{ $json.student.name }}",
"Score Percentage": "={{ $json.scorePercentage }}%"
},
"schema": [
{
"id": "Student Name",
"type": "string",
"display": true,
"required": false,
"displayName": "Student Name",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Roll No",
"type": "string",
"display": true,
"required": false,
"displayName": "Roll No",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Class",
"type": "string",
"display": true,
"required": false,
"displayName": "Class",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Q.1",
"type": "string",
"display": true,
"required": false,
"displayName": "Q.1",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Q.2",
"type": "string",
"display": true,
"required": false,
"displayName": "Q.2",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Q.3",
"type": "string",
"display": true,
"required": false,
"displayName": "Q.3",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Q.4",
"type": "string",
"display": true,
"required": false,
"displayName": "Q.4",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Q.5",
"type": "string",
"display": true,
"required": false,
"displayName": "Q.5",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Q.6",
"type": "string",
"display": true,
"required": false,
"displayName": "Q.6",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Q.7",
"type": "string",
"display": true,
"required": false,
"displayName": "Q.7",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Q.8",
"type": "string",
"display": true,
"required": false,
"displayName": "Q.8",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Q.9",
"type": "string",
"display": true,
"required": false,
"displayName": "Q.9",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Q.10",
"type": "string",
"display": true,
"required": false,
"displayName": "Q.10",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Q.11",
"type": "string",
"display": true,
"required": false,
"displayName": "Q.11",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Q.12",
"type": "string",
"display": true,
"required": false,
"displayName": "Q.12",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Q.13",
"type": "string",
"display": true,
"required": false,
"displayName": "Q.13",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Q.14",
"type": "string",
"display": true,
"required": false,
"displayName": "Q.14",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Q.15",
"type": "string",
"display": true,
"required": false,
"displayName": "Q.15",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Q.16",
"type": "string",
"display": true,
"required": false,
"displayName": "Q.16",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Q.17",
"type": "string",
"display": true,
"required": false,
"displayName": "Q.17",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Q.18",
"type": "string",
"display": true,
"required": false,
"displayName": "Q.18",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Q.19",
"type": "string",
"display": true,
"required": false,
"displayName": "Q.19",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Q.20",
"type": "string",
"display": true,
"required": false,
"displayName": "Q.20",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Correct",
"type": "string",
"display": true,
"required": false,
"displayName": "Correct",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Incorrect",
"type": "string",
"display": true,
"required": false,
"displayName": "Incorrect",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Score Percentage",
"type": "string",
"display": true,
"required": false,
"displayName": "Score Percentage",
"defaultMatch": false,
"canBeUsedToMatch": true
}
],
"mappingMode": "defineBelow",
"matchingColumns": [],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {},
"operation": "append",
"sheetName": {
"__rl": true,
"mode": "list",
"value": "gid=0",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/15nvRI8zcJ2n-QBXNk9xyd1GM8Vj7_1lJ3K7vO7Qfq3w/edit#gid=0",
"cachedResultName": "Sheet1"
},
"documentId": {
"__rl": true,
"mode": "list",
"value": "15nvRI8zcJ2n-QBXNk9xyd1GM8Vj7_1lJ3K7vO7Qfq3w",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/15nvRI8zcJ2n-QBXNk9xyd1GM8Vj7_1lJ3K7vO7Qfq3w/edit?usp=drivesdk",
"cachedResultName": "Students Result"
}
},
"typeVersion": 4.7
},
{
"id": "37946fab-f3c4-41d5-ab75-64570b720429",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
576,
2128
],
"parameters": {
"color": 4,
"width": 416,
"height": 256,
"content": "## Google Sheet\nhttps://docs.google.com/spreadsheets/d/15nvRI8zcJ2n-QBXNk9xyd1GM8Vj7_1lJ3K7vO7Qfq3w/edit?usp=sharing\n\n\n## Webhook Payload :\nForm-Data\nkey-name : file\nValue : Upload Img"
},
"typeVersion": 1
},
{
"id": "a81c3963-b846-440c-90e2-c13bd394713a",
"name": "Workflow Overview",
"type": "n8n-nodes-base.stickyNote",
"position": [
144,
1696
],
"parameters": {
"width": 420,
"height": 528,
"content": "## How it works\nThis workflow automates OMR (Optical Mark Recognition) answer sheet evaluation. Upload a student's answer sheet image via webhook, and AI analyzes the marked bubbles to extract answers. The system compares them against correct answers, calculates scores, and stores results in Google Sheets.\n\nKey features:\n- AI-powered bubble detection using Gemini\n- Automatic student detail extraction (name, roll number, class)\n- Question-by-question comparison with correct answers\n- Binary scoring (1/0) for each question\n- Real-time results via webhook response\n\n## Setup steps\n1. Configure Ollama credentials for Gemini AI model\n2. Set correct answers in \"Set Your Correct Answer\" node\n3. Connect Google Sheets OAuth2 credentials\n4. Test webhook with form-data (key: \"file\", value: OMR image)\n5. Verify results appear in Google Sheet"
},
"typeVersion": 1
},
{
"id": "4ab41919-537e-40a3-b051-e0bbe3a19b65",
"name": "Input Section",
"type": "n8n-nodes-base.stickyNote",
"position": [
576,
1696
],
"parameters": {
"color": 7,
"width": 264,
"height": 412,
"content": "## Input Section\nReceives OMR sheet image via webhook and sets up correct answer key for comparison."
},
"typeVersion": 1
},
{
"id": "b13f3985-fec0-41e0-95fa-5fa7501fcbb1",
"name": "AI Processing",
"type": "n8n-nodes-base.stickyNote",
"position": [
848,
1696
],
"parameters": {
"color": 7,
"width": 280,
"height": 412,
"content": "## AI Processing\nGemini AI analyzes the image to extract student details and marked answers from bubbles."
},
"typeVersion": 1
},
{
"id": "c75ce701-c5ac-441d-a9e2-2d3d2d29e284",
"name": "Answer Comparison",
"type": "n8n-nodes-base.stickyNote",
"position": [
1136,
1696
],
"parameters": {
"color": 7,
"width": 408,
"height": 412,
"content": "## Answer Comparison\n\nMerges student answers with correct answers, then calculates score and generates detailed results."
},
"typeVersion": 1
},
{
"id": "2622777b-a6b4-41c9-a33f-58fbaa7707a0",
"name": "Output Section",
"type": "n8n-nodes-base.stickyNote",
"position": [
1552,
1696
],
"parameters": {
"color": 7,
"width": 644,
"height": 412,
"content": "## Output Section\nSaves results to Google Sheets and returns JSON response with complete evaluation data."
},
"typeVersion": 1
}
],
"connections": {
"Merge Answer": {
"main": [
[
{
"node": "Calculate Result",
"type": "main",
"index": 0
}
]
]
},
"Analyze image": {
"main": [
[
{
"node": "Convert User Ans in Formate",
"type": "main",
"index": 0
}
]
]
},
"Calculate Result": {
"main": [
[
{
"node": "Append Result in Sheet",
"type": "main",
"index": 0
}
]
]
},
"Send Student Ans Img": {
"main": [
[
{
"node": "Analyze image",
"type": "main",
"index": 0
},
{
"node": "Set Your Correct Answer",
"type": "main",
"index": 0
}
]
]
},
"Append Result in Sheet": {
"main": [
[
{
"node": "Send Result Respond",
"type": "main",
"index": 0
}
]
]
},
"Set Your Correct Answer": {
"main": [
[
{
"node": "Merge Answer",
"type": "main",
"index": 0
}
]
]
},
"Convert User Ans in Formate": {
"main": [
[
{
"node": "Merge Answer",
"type": "main",
"index": 1
}
]
]
}
}
}
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About this workflow
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…
Source: https://n8n.io/workflows/12549/ — original creator credit. Request a take-down →
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