You will get Custom Computer Vision & OCR Pipelines for Images (OCR, Object Detection)
Rising Talent
Project details
This project delivers a production‑ready Computer Vision and Document AI pipeline that automates OCR, object detection, segmentation, and diagram understanding for your real‑world documents and images. It is built for teams that want to turn invoices, forms, contracts, PDFs, scans, UI screenshots, and technical diagrams into clean, structured data.
Using modern deep learning models (YOLOv8, RF‑DETR, SAM‑style segmentation, PaddleOCR) with Python, OpenCV, and FastAPI/Flask, you get high‑accuracy document processing and image processing wrapped in simple REST APIs or microservices. The solution supports key use cases like invoice processing, form recognition, ID / receipt parsing, table extraction, and diagram or flowchart parsing with arrow‑to‑node association.
Everything is designed to be production‑ready: Dockerized services, cloud‑ready deployment on AWS/GCP/Azure, and a clean architecture that fits into SaaS products or internal automation tools. Whether you need a focused computer vision/OCR model or a full end‑to‑end Document AI system, you get a custom pipeline aligned with your data, your business rules, and your existing stack.
Using modern deep learning models (YOLOv8, RF‑DETR, SAM‑style segmentation, PaddleOCR) with Python, OpenCV, and FastAPI/Flask, you get high‑accuracy document processing and image processing wrapped in simple REST APIs or microservices. The solution supports key use cases like invoice processing, form recognition, ID / receipt parsing, table extraction, and diagram or flowchart parsing with arrow‑to‑node association.
Everything is designed to be production‑ready: Dockerized services, cloud‑ready deployment on AWS/GCP/Azure, and a clean architecture that fits into SaaS products or internal automation tools. Whether you need a focused computer vision/OCR model or a full end‑to‑end Document AI system, you get a custom pipeline aligned with your data, your business rules, and your existing stack.
Machine Learning Tools
Amazon SageMaker, BERT, GPT-3, Keras, OpenCV, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, SQL, TensorFlowWhat's included
| Service Tiers |
Starter
$150
|
Standard
$450
|
Advanced
$850
|
|---|---|---|---|
| Delivery Time | 3 days | 7 days | 14 days |
Number of Revisions | 3 | 6 | Unlimited |
Number of Model Variations | 3 | 5 | 10 |
Number of Scenarios | 3 | 7 | 12 |
Number of Graphs/Charts | 20 | 50 | 0 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | |||
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$100
Additional Model Variation
+$100About Wasif
Data Scientist & ML Engineer | Computer Vision, LLMs, RAG
Lahore, Pakistan - 1:36 pm local time
𝗜 𝗯𝘂𝗶𝗹𝗱 𝗺𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝘁𝗵𝗮𝘁 𝗿𝗲𝗮𝗰𝗵 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗻𝗼𝘁𝗲𝗯𝗼𝗼𝗸𝘀. My computer vision pipeline hit 𝟬.𝟴𝟯 𝗺𝗔𝗣. My RAG recommender reached about 𝟴𝟵% 𝗺𝗮𝘁𝗰𝗵 𝗮𝗰𝗰𝘂𝗿𝗮𝗰𝘆. If you want models that hold up on real data, we should talk.
Most AI work stalls between a promising demo and something a business can actually rely on. That gap is where I do my best work: taking a model or an idea and turning it into a service with an API, tests, and monitoring that your team can use every day.
𝗪𝗵𝗮𝘁 𝗜 𝗱𝗲𝗹𝗶𝘃𝗲𝗿
• 𝗟𝗟𝗠𝘀, 𝗥𝗔𝗚 𝗮𝗻𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜. Retrieval over your own data, domain chatbots, and AI agents that call your APIs in a safe way, with source citations so answers stay grounded. I built a RAG job recommender at about 𝟴𝟵% 𝗺𝗮𝘁𝗰𝗵 𝗮𝗰𝗰𝘂𝗿𝗮𝗰𝘆 with LangChain and Pinecone.
• 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻. Object detection, segmentation, and OCR. I shipped a diagram reader that reached 𝟬.𝟴𝟯 𝗺𝗔𝗣 𝗮𝗻𝗱 𝟵𝟳% 𝗮𝗿𝗿𝗼𝘄-𝘁𝗼-𝗻𝗼𝗱𝗲 𝗮𝗰𝗰𝘂𝗿𝗮𝗰𝘆 using RF-DETR, SAM2, and PaddleOCR.
• 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗮𝗻𝗱 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀. Data cleaning, ETL, EDA, feature engineering, and clear data visualization. Predictive models with scikit-learn and XGBoost. You get plain results, not a wall of code.
• 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗠𝗟𝗢𝗽𝘀. Deployment with FastAPI, Docker, and AWS with CI/CD, so your model runs in production and stays there.
𝗛𝗼𝘄 𝗜 𝘄𝗼𝗿𝗸
I map the data flow, the APIs, and the infrastructure before I write code. I ship in small steps you can see. I keep the repo clean and documented. I reply fast and I tell you the truth about scope, including when something is not worth building.
𝗪𝗵𝗼 𝗜 𝘄𝗼𝗿𝗸 𝘄𝗲𝗹𝗹 𝘄𝗶𝘁𝗵
Founders and teams who have real data or a rough AI idea and need someone who can own it end to end, from the first data pull to a running production system.
𝗖𝗼𝗿𝗲 𝘀𝘁𝗮𝗰𝗸
𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗮𝗻𝗱 𝗥𝗔𝗚: OpenAI API, Anthropic API, LangChain, Pinecone, pgvector, embeddings, prompt engineering, AI agents
𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻: RF-DETR, SAM2, YOLO, PaddleOCR, OpenCV, PyTorch
𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗗𝗮𝘁𝗮: scikit-learn, XGBoost, LightGBM, Pandas, NumPy, SQL, Power BI
𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 𝗮𝗻𝗱 𝗠𝗟𝗢𝗽𝘀: FastAPI, Docker, AWS, Git, GitHub Actions, PostgreSQL
Send me a short note about your data and your goal, and I will tell you the fastest honest path to a working system.
Steps for completing your project
After purchasing the project, send requirements so Wasif can start the project.
Delivery time starts when Wasif receives requirements from you.
Wasif works on your project following the steps below.
Revisions may occur after the delivery date.
Step 1 – Requirements & sample data
You share 2–3 sample documents/images (PDFs, scans, diagrams) and describe which fields, objects, or relationships you want the system to detect or extract.
Step 2 – Solution design & model selection
A custom Computer Vision / Document AI pipeline is designed for your use case, selecting suitable models (YOLOv8, RF‑DETR, SAM‑style segmentation, PaddleOCR) plus post‑processing and validation logic.