Freelancers using Tesseract

Freelancers using TesseractFreelancers using Tesseract

Projects People

Dominik Oberriesser

maxHerzogenburg, Austria

The Architect of Impossible Solutions | Enhanced by AI

$5k+: Earned

3x: Hired

5.0: Rating

16: Followers

Expert

The Architect of Impossible Solutions | Enhanced by AI

Cover image for TCG OCR Scan & Shopify

TCG OCR Scan & Shopify Integration Problem: Listing large Pokémon card inventories manually was slow and error-prone. Each card required manual input for name, set, etc. Solution: We developed an OCR-powered system that identifies TCG cards from scans (Document Scanner, Pictures with Smarpthone), matches them to a database. The system uses Tesseract OCR & Azure Computer Vision for image recognition. Key Features: • Dual OCR pipeline (Tesseract + Azure Vision) • Automated card lookup via TCG API • Review queue for low-confidence scans • Auto-listing to Shopify with title, rarity, set & price • Error handling and manual approval interface Results: • 97% faster product listing workflow • Human review ensures 100% data accuracy • Supports bulk imports for large collections • Greatly improved speed and consistency in catalog updates

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Cover image for To pay my suppliers in different currency's, countries was a...

To pay my suppliers in different currency's, countries was always a pain. 150 invoices per month, manually... Wise is great to save on cost, but each invoice has to be manually entered or scanned. BatchPayer solves the issue. It scans up to 1k invoices in bulk, identifies by OCR the data for the transfer and puts them into the batch file format. Then you can export the CSV or push it by API to WISE to wire it. Working: 👍 OCR Scan - still improving multi language, currency etc. 👍Review System (Check if all infos are extracted correctly) 👍Payment System Working on: 🧠 API integration (working on compliance rules etc. as sending money by wire transfer is critical) 🧠 Smartphone App to add Scanner 🧠 Own extraction model & refine existing one by mismatch etc. Project Link (https://invoice-ocr-upload-for-wise-362.created.app/)

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Cover image for Laser Comparison Platform Problem: Finding the right laser e...

Laser Comparison Platform Problem: Finding the right laser engraver or cutter was difficult for me. Specs, prices, and models were spread across many manufacturer sites. I couldn’t easily compare key details like laser type, power, wavelength, or supported materials. Solution: I build a powerful comparison and search platform that helps users identify lasers matching their needs. The site combines a Bolt.com frontend with Supabase for structured product data, while n8n and Firecrawl handle scheduled price checks and data validation to keep listings accurate and up to date. Key Features: • Over 100+ filter options • Live database powered by Supabase • Data validation & more via n8n + Firecrawl • Custom compatibility scoring algorithm Tools Used: Bolt.com Supabase n8n Firecrawl Custom Code

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Cover image for Centralized Sales Automation System 🔍 Problem The client la...

Centralized Sales Automation System 🔍 Problem The client launches dozens of new products each month. Every product had its own Zapier workflow. Managing hundreds of automations became slow, error-prone, and hard to maintain. 💡 Solution We built a centralized automation system using n8n with a custom webhook plugin. All orders are now sent to a single webhook that references a Google Sheet for configuration. Key Automations: • Single webhook endpoint for all products • Dynamic routing via Google Sheets • Auto-updates to CRM, Kajabi, Discord... • No-code management - changes made directly in Sheets ⚙️ Results: 🕒 Replaced 300+ Zaps with one scalable workflow ⚙️ 95% less maintenance 🚀 New launches need only one new row in Sheets 🧰 Tools Used: n8n Google Sheets ActiveCampaign Discord Apps Custom Webhook Plugin Forum API Kajabi

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Zouhir Laoulaou

Software Developer | AI Developer

$1k+: Earned

2x: Hired

5.0: Rating

20: Followers

Software Developer | AI Developer

Cover image for Rehearsal Buddy: Browser-Based Rehearsal Partner Prototype

Rehearsal Buddy: Browser-Based Rehearsal Partner Prototype

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10

Cover image for PostSchedule.xyz : AI Avatars and Scheduling on Tiktok

PostSchedule.xyz : AI Avatars and Scheduling on Tiktok

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Cover image for LeadCapture MVP

LeadCapture MVP

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Cover image for Declicy: AI-Powered Fitness Coaching Solution

Declicy: AI-Powered Fitness Coaching Solution

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Armughan Shahid

proIslamabad, Pakistan

AI SaaS Dev | LLMs, Agents, Voice & Automation | Web, Mobile

New to Contra

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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Cover image for We Step Together - Step-to-Donation Mobile App

We Step Together - Step-to-Donation Mobile App

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1

Cover image for Development of CarlsbergHub & ParkingHub

Development of CarlsbergHub & ParkingHub

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1

Cover image for Development of HonestDog Platform

Development of HonestDog Platform

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3

AI-powered enterprise applications that scale and innovate

$10k+: Earned

2x: Hired

3: Followers

AI-powered enterprise applications that scale and innovate

Cover image for License Plate Recognition System with YOLOv4 and Tesseract

License Plate Recognition System with YOLOv4 and Tesseract

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3

Cover image for DVHEConverter

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Cover image for Awesome macOS Open Source Applications

Awesome macOS Open Source Applications

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Python | Web Scraping | Automation | OCR | API Integration

Python | Web Scraping | Automation | OCR | API Integration

Cover image for Advanced PDF Data Extraction Engine

Advanced PDF Data Extraction Engine

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Cover image for Scalable & Reliable AWS-Powered API Service

Scalable & Reliable AWS-Powered API Service

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25

Cover image for High-Volume Web Scraper with Multi-Layer CAPTCHA Bypass

High-Volume Web Scraper with Multi-Layer CAPTCHA Bypass

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14

Cover image for Geographical Data Scraper for Dynamically Loaded Content

Geographical Data Scraper for Dynamically Loaded Content

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11

AI Engineer | Document AI, Voice & Vision Systems

New to Contra

AI Engineer | Document AI, Voice & Vision Systems

Cover image for Developed a system that replaces

Developed a system that replaces traditional OCR by using AI to interpret full-page handwritten documents. Instead of extracting text line-by-line, the system: Identifies document type Uses a structured JSON contract to define required fields Sends full-page images to an AI model Returns clean, structured data (including multi-record tables) Supports: Variable number of records per document Field validation and normalization Spreadsheet-ready outputs This approach significantly improves accuracy compared to OCR-based solutions.

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Cover image for ARJAN — My Personal AI

ARJAN — My Personal AI Consulting Platform, Built on Zo Computer I'm Ali Beheshti — AI consultant, founder of iTAB, and professor of Data Science Ethics at George Washington University. ARJAN is where those three identities meet. 🔗 Live site: https://arjan-site-ab.zocomputer.io/ What I built on Zo: → Mission Readiness Score — visitors adjust 4 live sliders across Quality Engineering, Section 508, AI Governance, and Mission Security. An animated ring scores them in real time, identifies their top gap and strongest area, and one click sends the full assessment into my contact flow → Live TTS narration on every capability card with synced ASL overlay — Section 508 compliant, built for everyone → Gmail-powered contact workflow via Zo's native integration — 4 simultaneous email paths on every submission. No third-party tools. Just Zo. → Capability cards reshuffle on every visit — no two visits are identical → Pixel-aware logo hover animation with inline sound toggle My philosophy: AI should free people to focus on what matters — not replace them. Every feature on this site exists to serve the visitor. That is the only reason to build anything. #zocomputerchallenge

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Cover image for Designed a multimodal AI system

Designed a multimodal AI system combining voice input, document understanding, and structured data extraction. The system processes full-page documents and spoken input together, allowing flexible and efficient data capture. Key capabilities: AI-based document interpretation (beyond OCR) Voice-driven interaction and data entry Structured JSON outputs for downstream systems Support for multi-record extraction and validation This approach enables fully automated workflows in data-heavy environments and field operations.

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Cover image for Developed an AI-powered platform for

Developed an AI-powered platform for automating field data collection, analysis, and reporting. The system integrates multiple input methods, including handwritten sheets, voice input, and direct data entry. Key capabilities: Automated data extraction and validation AI-assisted report generation Workflow tracking and project management Customizable outputs for engineering and technical teams Designed to reduce manual work and improve accuracy in field operations.

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Sebastian Van Rooyen

Johannesburg, South Africa

Professional Asp Dotnet Developer -- Web | Desktop | API

Professional Asp Dotnet Developer -- Web | Desktop | API

Cover image for Snipper Master - ASP dotnet Desktop Application

Snipper Master - ASP dotnet Desktop Application

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1

Cover image for React + Asp Dotnet Core Web API

React + Asp Dotnet Core Web API

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5

Cover image for Agentic backend with Blazor

Agentic backend with Blazor

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2

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Aldrin Caballero

Quezon City, Philippines

Full-stack developer for AI-powered marketplaces

5.0: Rating

8: Followers

Full-stack developer for AI-powered marketplaces

Cover image for AI filter text from PDF

AI filter text from PDF using OCR

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Cover image for Beauty fasion

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25

Cover image for NEXTPLAY is a soccer training

NEXTPLAY is a soccer training and talent discovery mobile app designed to connect young players with coaches and opportunities.

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Cover image for I worked there as a

I worked there as a React Native engineer for about 1.5 years, focused primarily on the mobile experience for long-form audio and editorial content. My work centered on content delivery, playback reliability, and offline support — including designing a caching layer that balanced storage limits with user expectations (e.g., partial downloads, smart eviction, and content freshness). I also collaborated closely with backend and product teams to align CMS structure with how content is consumed in-app.

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