Hire the Best OCR Tesseract Specialists

I help businesses stop wasting hours on manual document processing by building Python, OCR, AI, and automation systems that convert messy documents into clean, structured, usable data. 🚀 My work is not just “OCR” or simple copy-paste automation. I build practical tools, Python scripts, REST APIs, and MVP workflows that can extract data from PDFs, scanned documents, invoices, receipts, purchase orders, bank statements, forms, reports, and images — then clean, validate, review, and export the results into Excel, CSV, JSON, Google Sheets, databases, or API-ready formats. ⚙️ What I can help you build: ✅ Python scripts for PDF, OCR, image, and data extraction automation ✅ PDF to Excel / CSV / JSON conversion workflows ✅ OCR pipelines using Google Vision OCR, Tesseract, OpenCV, PyMuPDF, and AI models ✅ Invoice, receipt, purchase order, bank statement, and form parsing systems ✅ REST APIs for document upload, processing, extraction, and export ✅ MVP tools for document processing platforms and internal business automation ✅ Human-in-the-loop review systems to improve accuracy before final export ✅ Excel automation, data cleaning, matching, validation, and reporting ✅ Google Sheets automation and structured data workflows ✅ Web scraping and API-based data collection when needed ✅ Custom automation tools that replace repetitive manual work 🧠 My main advantage: I combine strong manual data extraction experience with real Python automation skills. That means I understand both sides of the problem: 1. The accuracy needed when dealing with messy, real-world documents 2. The technical automation needed to process files faster and more reliably at scale I have worked on OCR and document automation projects involving scanned PDFs, financial documents, purchase orders, invoices, receipts, bank statements, forms, Google Drive OCR pipelines, Excel automation, structured data conversion, and AI-assisted document parsing. I also build review workflows where uncertain values are flagged for human review instead of blindly exporting incorrect data. This is especially useful for businesses that need high accuracy, auditability, and clean final outputs. 💡 Example workflows I can build: A user uploads PDFs or scanned images → the backend extracts the required fields → low-confidence or unclear values are sent to a review screen → the final approved data is exported to Excel, CSV, JSON, Google Sheets, or sent through a REST API. Another example: A business receives invoices, POs, or reports every day → a Python automation processes the files → extracts key fields and line items → validates the data → highlights missing or uncertain values → generates a clean Excel report ready for use. I care about building solutions that are practical, accurate, and useful in real daily work — not just scripts that work on one perfect sample file. If you have sample files, send me 1–3 examples and I can review the structure, suggest the best workflow, and explain the expected accuracy, cost, and implementation approach.

Hi! I work with image and video data annotation for computer vision projects. I focus on clean, accurate labels and always follow project guidelines carefully. I have experience with bounding boxes, polygons, semantic segmentation, and image masking. I understand how annotation quality affects model performance, so I pay close attention to details and edge cases. Tools I use: CVAT | Roboflow | LabelMe | MakeSense.ai and any other I can quickly adapt to new annotation platforms if needed. If you need a reliable annotator who delivers consistent and well-structured datasets, I’m ready to help. Skills: -Image & Video Annotation -Bounding Boxes -Polygon Annotation -Semantic Segmentation -Image Masking As a competitive and quick learner, I ensure top-notch outputs. Your project deserves the best start, and I’m here to provide it through precise and reliable data annotation.

7+ years building production computer-vision, OCR, edge-AI, and AI backend systems where accuracy, latency, and reliability matter. Typical work includes defect detection, live video/object tracking, on-device model optimization, document/OCR pipelines, OpenAI/RAG integrations, and launch rescue for AI products that need evaluation, observability, and clean API architecture. My strongest fit is a project with real data, a broken or uncertain pipeline, and measurable acceptance targets: accuracy, false positives, latency, edge-device constraints, cost, security, or launch readiness. Core work: - Computer vision / OCR: detection, segmentation, tracking, image analysis, OCR extraction, validation, reviewer workflows - Edge AI: ONNX/TensorRT conversion, model optimization, quantization-aware deployment, mobile/on-device inference - AI backend: OpenAI API, RAG, VLMs, source traces, confidence checks, FastAPI, PostgreSQL - Production delivery: tests, logging, monitoring, cloud deployment, handoff docs I am a good fit when AI output must be accurate, fast enough for production, reviewable, logged, and reliable enough for real business use.

Do you have an AI vision that needs to become a real, working product? I don't just build models; I engineer complete, scalable solutions that turn data into actionable insights and automation. For over five years, I've specialized in bridging the gap between cutting-edge Artificial Intelligence (AI) research and robust software that delivers real-world value. My core expertise lies in computer vision and machine learning, but my skill set is full-stack. This means I can own your project from the initial data pipeline, through model training and optimization, all the way to deploying a polished desktop application or a secure enterprise API. I thrive on building tools that work seamlessly for end-users, whether it's a retail manager, a traffic controller, or a sports coach. My strongest suit is developing intelligent systems that "see" and understand the world. I've built a retail analytics platform (CrowdIQ) that transforms standard CCTV into a source of business intelligence, tracking customer demographics and behavior. In the sports domain, I created PadelIQ, an analytics engine that uses computer vision to track player movement, posture, and court coverage from match footage, providing real-time coaching feedback. For public safety, I developed a traffic management system (OmniRoad AI) using advanced object detection for real-time accident and congestion monitoring. Beyond computer vision, I architect full-scale data science pipelines. A prime example is my telecom churn prediction project, where I built a machine learning model to identify at-risk customers and paired it with an interactive Power BI dashboard. This end-to-end approach—from data analysis to a clear visualization of insights—ensures the model's findings directly inform business strategy and retention actions. I also develop the tools and infrastructure that power AI applications. I've built secure, enterprise-grade systems like DevelmoGPT, a RAG-based LLM that allows for secure, semantic search over private company documents. From creating simple utilities like PDF-to-audio converters to designing complex role-based access systems, I ensure the foundation of any AI solution is reliable, secure, and maintainable. My process is collaborative and results-driven. I start by deeply understanding your business problem, not just the technical requirement. We'll then iterate through prototyping, development, and testing to ensure the final product not only meets specs but also delivers tangible ROI. I communicate clearly at every stage, providing demos and documentation so you're never in the dark. Let's connect. Share your project idea or challenge, and I'll provide a clear outline of how we can leverage AI, machine learning, or computer vision to build your intelligent solution. Click the invite button to start the conversation. /// The following is just for SEO. You can ignore it /// #computer vision #computer vision engineer #computer vision OpenCV #machine learning computer vision #deep learning computer vision #computer vision machine learning #machine learning python #nlp machine learning

Computer Vision Engineer specializing in real-time detection systems across surveillance AI, manufacturing inspection, medical imaging, and smart city infrastructure — deployed to production. I build end-to-end: camera input, model training, TensorRT optimization, edge or cloud deployment, and backend integration. Not prototypes. Working systems. WHAT I HAVE SHIPPED: Multi-tenant AI surveillance platform with 8 real-time detection agents — intrusion, loitering, occupancy, vehicle counting, LPR, fire/smoke detection, entry after hours, and late checkout. YOLO11s + PaddleOCR on RunPod GPU, RTSP stream management via MediaMTX, FastAPI backend on DigitalOcean, CI/CD via GitHub Actions with a self-hosted runner. Supabase for auth and Postgres. Full production deployment. Spine X-ray segmentation system — 25-class vertebrae labeling (C1 through L5) with automated clinical metric extraction: disc height, segment angle, neutral listhesis, interspinous gap. Dual architecture: ResNet34 U-Net baseline and a custom ConvNeXt-Tiny encoder with CBAM attention trained on real X-rays and synthetic DRR data from CT volumes. 0.92 Dice on full-spine segmentation. Real-time defect detection on NVIDIA Jetson Xavier for a food and beverage production line. TensorRT FP16 inference, zero cloud dependency, PLC integration for automated pass/fail. Defect catch rate improved significantly over manual baseline. LiDAR-camera sensor fusion pipelines for smart city traffic monitoring — ROS, DeepStream, NVIDIA Jetson, production-deployed infrastructure. CORE CAPABILITIES: → Detection, segmentation, tracking: YOLO variants, SAM2, custom PyTorch → TensorRT optimization: FP16/INT8 quantization, ONNX export, engine serialization → Edge deployment: Jetson Nano, NX, Orin, Xavier — hardware bring-up to production → Medical imaging: U-Net architectures, multi-class segmentation, DICOM, clinical metrics → Surveillance AI: multi-agent detection, RTSP streams, LPR, event-based alerting → Industrial integration: FastAPI, MQTT, EMQX, Azure IoT Hub, AWS S3, Docker, Supabase INDUSTRIES: Manufacturing quality control — food and beverage, general production lines Surveillance and security — multi-camera platforms, license plate recognition, event detection Medical imaging — vertebral segmentation, clinical measurement pipelines Smart city and traffic — LiDAR-camera fusion, intrusion detection, infrastructure monitoring I communicate clearly, surface blockers early, and deliver systems that run reliably in your environment.

I build production-grade OCR and AI-powered document extraction pipelines that turn scanned PDFs, images, invoices, and unstructured documents into clean, structured, usable data accurately and at scale. Whether you have 10 documents or 100,000 I deliver automated solutions that save time, reduce manual effort, and integrate directly into your existing workflows. 🔧 Tools & Technologies I Use Daily: - OCR Engines: Tesseract, PaddleOCR, EasyOCR, AWS Textract, Google Cloud Vision API - Computer Vision: OpenCV, YOLO, image preprocessing (noise reduction, skew correction, deskewing) - AI & ML Pipelines: Python, FastAPI, LangChain, custom NLP models for post-OCR correction - Data Output: JSON, CSV, Excel, structured databases (PostgreSQL, MongoDB) - Automation: end-to-end document pipelines, API integrations, cloud deployments (AWS, GCP) 📄 What I Extract From: → Invoices → Bank statements → Legal contracts → Medical forms → ID cards → Handwritten documents → Logistics labels → Financial reports → Scanned books 🏭 Industries I've Served: → Fintech → Healthcare → Legal → Logistics → E-commerce → Insurance 📊 Results I Deliver: ✔ 98%+ OCR accuracy on complex, low-quality scans ✔ Automated pipelines processing 5,000+ documents weekly ✔ Reduced manual data entry time by 80–90% for clients ✔ End-to-end solutions from raw image → structured database I don't just extract text I build intelligent systems that understand your documents. If you have a document challenge, send me a sample file and I'll tell you exactly how I'd solve it before you even hire me.