Freelancers using Tesseract in IslamabadFreelancers using Tesseract in Islamabad
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Armughan Shahid's avatar
Armughan Shahid
proIslamabad, Pakistan
AI SaaS Dev | LLMs, Agents, Voice & Automation | Web, Mobile
1x
Hired
5.0
Rating
3
Followers
AI SaaS Dev | LLMs, Agents, Voice & Automation | Web, Mobile
Cover image for OCR Receipt Parsing Microservice (AI-Powered
OCR Receipt Parsing Microservice (AI-Powered Backend System) Most receipt-based systems fail because the data is messy, inconsistent, and spread across formats that machines don’t naturally understand. People don’t realise it, but the real problem isn’t capturing receipts, it’s turning them into reliable, structured data that can actually be used. This system removes that friction entirely. You send a receipt (image or PDF), and it comes back as clean, structured JSON ready to plug into any workflow. The core problem it solves: Receipt data is chaotic. Different formats, inconsistent naming, missing structure, and OCR noise make it hard to extract anything usable. Even when OCR works, the output is raw text, not something you can build logic on top of. This project builds a full processing layer that doesn’t just read receipts, it understands and standardises them. What was built: A backend microservice that acts as a structured data engine for receipts. The system accepts images or PDFs via an API, runs OCR, extracts merchant details, dates, totals, and line items, and converts everything into a strict JSON schema. But the real value sits in what happens after OCR. A normalization layer cleans and standardises item names so inconsistent inputs like “BANANA”, “Bananas”, or “Banana 1lb” all map to a single canonical item. Quantities and prices are cleaned, structured, and validated so the output becomes consistent across different stores and formats. The system can also plug directly into Airtable, pushing structured items into a live database, enabling automated workflows like pantry tracking, expense logging, or analytics pipelines without needing a full backend system. Everything is exposed through a simple /parse-receipt API, making it easy to integrate into mobile apps, SaaS products, or internal tools. Technical architecture: FastAPI-based microservice designed for simplicity and performance, with OCR powered by Tesseract or cloud services like AWS Textract and Google Vision depending on accuracy requirements. The parsing layer combines rule-based extraction with AI-assisted cleanup to handle real-world receipt noise. The system is fully containerized using Docker, deployable on platforms like Render or Heroku, and comes with OpenAPI (Swagger) documentation for quick testing and integration. Designed as a stateless service, it avoids database complexity and instead integrates with external systems (like Airtable), making it lightweight and easy to scale. Business value built in: This isn’t just an OCR tool, it’s a data standardization engine. The same system can power expense tracking apps, inventory systems, meal planning products, or financial analytics platforms. Because the parsing and normalization layers are modular, the microservice can be exposed as a standalone API, creating opportunities for reuse across multiple products or even external licensing.
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168
Cover image for Milematcher - Realtime Competitive Running Platform
Milematcher - Realtime Competitive Running Platform
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13
Cover image for Share the Light - Role-Based Tutoring Platform
Share the Light - Role-Based Tutoring Platform
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10
Cover image for We Step Together - Step-to-Donation Mobile App
We Step Together - Step-to-Donation Mobile App
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12
Tesseract(1)
Syeda Fiza Gilani's avatar
Syeda Fiza Gilani
Islamabad, Pakistan
AI Solutions Architect & Automation Engineer
New to Contra
AI Solutions Architect & Automation Engineer
Cover image for DocMorph AI is a full-stack
DocMorph AI is a full-stack web application designed to eliminate manual data entry. It ingests locked, scanned PDFs, runs advanced Optical Character Recognition (OCR), and reconstructs the data into clean, editable Microsoft Word documents. The Challenge: Standard OCR scripts frequently fail or crash the server when attempting to process large, multi-page PDFs due to massive RAM consumption. The client needed a lightweight, scalable solution that wouldn't time out during heavy workloads. The Solution: I architected a decoupled monorepo system. The frontend was built with React for a seamless, drag-and-drop user experience. For the backend, I engineered a custom Node.js/Express.js server utilizing Tesseract.js. To ensure stability, I developed a custom text-chunking algorithm that slices the PDF into manageable data streams, processes them safely, and stitches the final layout together into a downloadable .docx file.
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11
Cover image for Designed and built an automated
Designed and built an automated lead management engine that eliminates manual lead qualification and speeds up client outreach. Key Features & Implementation: Form & Webhook Processing: Captures incoming client submissions (budget, timeline, project scope) in real time. AI Evaluation & Scoring: Integrates Google Gemini via n8n to analyze project requirements, calculate lead priority scores, and run automated duplicate checks. Multi-Channel Actions: Sends customized follow-up proposals to clients, pushes real-time priority alerts to Slack channels, and updates lead tracking sheets dynamically.
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Cover image for I built this n8n workflow
I built this n8n workflow that implements a Retrieval-Augmented Generation (RAG) AI Agent designed to answer queries grounded in private company documents without hallucinating. Trigger: Initiated when a user chat message is received. AI Agent Core: Coordinates the response using an LLM backbone. Google Gemini Chat Model & Simple Memory: Powers conversational logic and tracks dialogue context across the session. Qdrant Vector Store & Embeddings (Gemini): Retrieves relevant context from indexed internal documents.
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Cover image for I built an n8n automated
I built an n8n automated lead routing workflow that instantly qualifies, enriches, and routes high-intent inbound leads within seconds. HTML Webhook Form: Captures form submissions containing lead details like name, work email, job title, and company name. SerpApi Enrichment: Pulls real-time web data and background information on the target company. AI Model Evaluation: Uses an AI model to analyze the data, score the lead's intent, and generate customized talking points. HubSpot CRM Integration: Automatically creates or updates the contact record inside HubSpot. Slack Alert: Broadcasts a formatted rich-text notification to Slack with the AI lead score and strategic angles for the sales team.
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Tesseract(1)