Easy OCR

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EasyOCR

EasyOCR is a font-dependent printed character reader based on a template matching algorithm. It has been designed to read any kind of short text part numbers, serial numbers, expiry dates, manufacturing dates, lot codes, printed on labels or directly on parts.

General
Relese date	June 09, 2019
Type	Character reader template matching algorithm

EasyOCR Libraries

Discover EasyOCR

EasyOCR Paper EasyOCR Original Generative Adversarial Network Paper
EasyOCR repository Ready-to-use OCR with 80+ supported languages and all popular writing scripts including: Latin, Chinese, Arabic, Devanagari, Cyrillic, etc.
EasyOCR DEMO EasyOCR demo from Jaided AI who created this open source library

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EasyOCR AI technology page Hackathon projects

Discover innovative solutions crafted with EasyOCR AI technology page, developed by our community members during our engaging hackathons.

Salience

Auto-clippers like ShadowPlay and Medal save everything — boring cleanup kills included. Salience is a personalized highlight-ranking layer that learns which moments YOU actually care about: one-pump shotgun kills, snipes, clutches, build fights, or Victory Royales. How it works: Salience watches the folder where clips are already saved, samples keyframes, and extracts structured labels (weapon, elimination type, context, victory). A ranked review inbox lets you mark clips favorite / keep / boring / skip, and a lightweight personal ranker updates instantly from that feedback plus an 18-question taste questionnaire. The AI story is teacher–student distillation. During development, Qwen VLM on Fireworks AI labels clips with 28 constrained highlight attributes and event timestamps. Those teacher labels train a local student: a MobileNetV3-small backbone with an event locator head, per-event weapon/evidence heads, and context heads, exported to ONNX and fused with local OCR (RapidOCR reads the weapon HUD and victory banner). On an 81-clip held-out eval, the student reaches ~0.80 mean per-label agreement with the VLM teacher — enemy-visible F1 0.97, elimination F1 0.93, victory F1 0.89 — while running entirely on CPU with no API key and no upload. Inference is sub-second per clip; the full pipeline including video decoding is ~30 s per clip. Privacy is the point: the shipped ranking loop never needs to upload gameplay. The cloud teacher is an optional development-time tool, and the same harness can route to AMD Developer Cloud (Qwen2.5-VL via vLLM) as the teacher endpoint.

HealthOS — Turn Lab Reports Into Real Answers

The average person typically receives a bunch of numbers in their lab report with no idea what is normal, no understanding of improvement, and no quick method to query their lab report — without having to search the web at 2 am and risking a site that’s not reputable. HealthOS fixes that. Simply upload a lab report (PDF file or even a picture) and HealthOS automatically pulls out all the values, highlights anything outside a healthy range and collapses it into a running health score that will update with each upload as it gets older. Each report follows on from the previous report and rather than just a single disconnected ‘snapshot' of progress, you are able to get an actual ‘snapshot' of improvement or not. It's accompanied by an AI chat assistant that already knows about your profile, medications, and lab history, allowing you to pose real questions to your bot and receive answers based on your own data, rather than general search data. HealthOS is a full stack application: OCR-powered report parsing, FastAPI backend, Structured health entries tracked over time in a PostgreSQL database, JWT-authenticated user accounts, with a Fireworks AI-powered assistant, and a React frontend, deployed end-to-end on free infrastructure (Vercel, Railway, Neon). We created HealthOS because the way we see it, knowing your health shouldn't require a medical degree or a doctor on speed dial — it should be something your data does for you.

Clausify AI

Clausify AI solves a real enterprise problem: procurement teams spend 4-6 hours manually cross-referencing contracts, invoices, and quotations — and still miss critical discrepancies that cost thousands. Upload your documents and Clausify instantly delivers: cross-document conflict detection (e.g., "invoice charges $525/unit but the contract says $425 — $13,144 overcharge"), risk analysis with severity ratings and source citations, side-by-side comparison matrices with clear winners, executive summaries written for decision-makers, and an AI chat copilot grounded in your documents with real-time streaming responses. The technical architecture runs 5 parallel LLM calls via asyncio.gather on AMD Instinct MI300X hardware through Fireworks AI, using a tiered model strategy: deepseek-v4-flash for deep reasoning and gpt-oss-120b (AMD-optimized) for structured extraction. The RAG pipeline uses 600-token chunks with 80-token overlap, all-MiniLM-L6-v2 embeddings, and ChromaDB vector search with top-12 retrieval. Key engineering highlights: consolidated conflict detection in O(1) LLM calls regardless of document count, 5-strategy JSON parsing for robust LLM output handling, persistent session storage surviving container restarts, auto-retry with exponential backoff, glassmorphism UI with full mobile responsiveness (320px-1920px), and professional PDF/DOCX report export. Built by Team Clausify: Rhenmart Dela Cruz ( Full Stuck AI Orchestrator Lead, Product Designer & ML ), Julie Ann Tiron (Backend & AI Research), Mica Pauline Calingo (Backend Support & Research), and Reymark Panes (QA Testing & Optimization).

Synapse Corp AI

Synapse AI is an enterprise-grade multi-agent workflow automation platform designed to simulate how real organizations operate using autonomous AI agents. The platform includes specialized agents such as HR, CTO, CFO, CEO, and Risk Management agents that collaborate intelligently to perform tasks like AI-driven interviews, candidate evaluation, operational analysis, workflow automation, and executive decision-making. Unlike traditional AI assistants or single-agent chatbots, Synapse AI focuses on collaborative intelligence where multiple AI agents communicate, reason, and coordinate together to solve complex organizational workflows in real time. The system supports multimodal interactions including text, documents, reports, and speech inputs, allowing users to simulate real enterprise environments and automate time-consuming operational processes. For example, users can conduct AI-powered HR interviews, upload business reports for executive analysis, or generate strategic recommendations through coordinated AI agent discussions. Technically, the platform is built using Next.js, FastAPI, Gemini AI, Speechmatics, Supabase, Docker, and Vultr cloud infrastructure. The architecture uses scalable distributed services, asynchronous processing, and modular AI orchestration to ensure reliability, low latency, and production-style deployment readiness. Synapse AI demonstrates how autonomous AI systems can function like real organizational teams, helping businesses improve operational efficiency, reduce repetitive manual work, accelerate decision-making, and create scalable intelligent enterprise workflows for the future of AI-driven organizations.

ContractIQ: AI Contract Analysis & Risk Scorer

Reviewing dense legal contracts manually takes hours of senior legal counsel time and costs thousands of dollars, creating a major bottleneck for sales, procurement, and startups. ContractIQ solves this by utilizing Google Gemini 2.5 Flash to automatically audit and analyze any contract in under 20 seconds with human-attorney level precision. ContractIQ provides a comprehensive suite of legal analysis tools: 1. Intelligent Risk Scorer: Assesses the contract on a 1-10 scale and details specific liabilities, severity levels, and concrete mitigation steps. 2. Automated Clause Analyzer: Maps, categorizes, and summarizes critical clauses (e.g., Liability, IP, Termination) and classifies them as favorable or unfavorable. 3. Interactive AI Attorney Chat: A context-aware chatbot allowing users to query contract terms, retrieve text snippets, and receive immediate legal explanations. 4. Red Flags & Recommendations: Surface hidden legal landmines instantly and get drafted alternative language ready for negotiations. 5. Multimodal Image Parsing: Uses Google Gemini's vision capability to parse scanned PDFs and images seamlessly. Built with a premium enterprise design system using Next.js 14, Tailwind CSS, TypeScript, and Firebase. ContractIQ represents a production-grade, state-of-the-art solution that unlocks instant legal intelligence for any size enterprise.