Extract & Categorize Receipt Data with Google OCR, OpenRouter AI & Telegram — Workflow n8n

Moyen complexité Déclencheur13 nœuds🏷️ Invoice Processing👁 1,536 vuespar Khairul Muhtadin

Aperçu

Effortlessly track your expenses with MoneyMate, an n8n workflow that transforms receipts into organized financial insights.

Upload a photo or text via Telegram, and let MoneyMate extract key details—store info, transaction dates, items, and totals—using Google Vision OCR and AI-powered parsing via OpenRouter.

It categorizes expenses (e.g., Food & Beverages, Transport, Household) and delivers a clean, emoji-rich summary back to your Telegram chat. Handles zero-total errors with a friendly nudg

Nœuds utilisés

HTTP RequestTelegramCodeBasic LLM ChainStructured Output ParserOpenRouter Chat Model

Aperçu du workflow

📲 Telegram Trigger
Receives user messages (text or photo) from Telegram to
🔍 Check for Image
Determines whether the input is an image or text to rou
MoneyMate is free to use!
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📬 Send Expense Summary
Sends a summary of the recognized receipt or input to t
📝 Extract Text Input
Handles plain text inputs when no image is provided by
📊 Parse Receipt Data
Converts AI output into structured JSON with store, tra
📦 Get Telegram File
Fetches the file ID of a receipt image uploaded via Tel
⬇️ Download Image
Downloads the receipt photo and prepares it for text ex
🖼️ Text Extractor
Extract text from given image
🧾 Format Message
Creates a clear summary of expenses with store, date, i
🧠 AI Categorizer
Uses AI to classify input into categories like income o
❗ Send Error Message
Sends a warning to the user if the input is invalid or
🚫 Check Invalid Input
Detects and flags incorrect or zero-value entries befor
🤖 OpenRouter AI Model
Configures the language model to extract structured inf
modelparser
Format Summary Message
Get Telegram File
Download Image
T
Telegram Trigger
AI Categorizer
C
Check Invalid Input
E
Extract Text Input
C
Check for Image
Receipt Parser
AI Analyzer
Send Error Message
Send Expense Summary
E
Extract Value From Image
13 nodes13 edges

Comment ça fonctionne

  1. 1

    Déclencheur

    Le workflow démarre avec un déclencheur déclencheur.

  2. 2

    Traitement

    Les données transitent par 13 nœuds, connecting chainllm, code, httprequest.

  3. 3

    Sortie

    Le workflow termine son automatisation et livre le résultat à la destination configurée.

Détails des nœuds (13)

HT

HTTP Request

httpRequest

#1
TE

Telegram

telegram

#2
CO

Code

code

#3
BA

Basic LLM Chain

n8n-nodes-langchain.chainLlm

#4
ST

Structured Output Parser

n8n-nodes-langchain.outputParserStructured

#5
OP

OpenRouter Chat Model

n8n-nodes-langchain.lmChatOpenRouter

#6

Comment importer ce workflow

  1. 1Cliquez sur le bouton Télécharger JSON à droite pour enregistrer le fichier du workflow.
  2. 2Ouvrez votre instance n8n. Accédez à Workflows → Nouveau → Importer depuis un fichier.
  3. 3Sélectionnez le fichier extract-categorize-receipt-data-with-google-ocr-openrouter-ai-telegram téléchargé et cliquez sur Importer.
  4. 4Configurez les identifiants pour chaque nœud de service (clés API, OAuth, etc.).
  5. 5Cliquez sur Tester le workflow pour vérifier que tout fonctionne, puis activez-le.

Ou collez directement dans n8n → Importer depuis JSON :

{ "name": "Extract & Categorize Receipt Data with Google OCR, OpenRouter AI & Telegram", "nodes": [...], ...}

Intégrations

chainllmcodehttprequestiflmchatopenroutern8n-nodes-tesseractjs.tesseractnodeoutputparserstructuredsettelegramtelegramtrigger

Obtenir ce workflow

Téléchargez et importez en un clic

Télécharger JSONVoir sur n8n.io
Nœuds13
Complexitémedium
Déclencheurtrigger
Vues1,536

Créé par

Khairul Muhtadin

Khairul Muhtadin

@khmuhtadin

Tags

chainllmcodehttprequestiflmchatopenroutern8n-nodes-tesseractjs.tesseractnodeoutputparserstructuredsettelegramtelegramtrigger

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🔗 Webhook·12 nodes