Machine-Learning on MOZA

Computer Vision Labs: OCR, Object Detection, Segmentation and Generative AI

Wed, 14 Oct 2026 00:00:00 +0000

Overview

Computer Vision Labs is a collection of three practical experiments in computer vision and generative AI. The project combines image preprocessing, pretrained vision models, and diffusion-based generation to solve different tasks with separate workflows.

The labs cover:

OCR with image preprocessing
Object detection, segmentation, and relative depth estimation
AI-powered interior redesign

1. OCR and Image Preprocessing

The first lab uses OpenCV and Tesseract OCR to extract text from a noisy image. The workflow improves the input before OCR rather than sending the raw image directly to Tesseract.

Autonomous Vehicle Perception

Sun, 01 Mar 2026 00:00:00 +0000

Overview

A computer-vision project developed as part of my Master’s studies.

The project explores perception for autonomous-vehicle scenarios, including situations such as driver distraction and passenger posture.

Technology Stack

Python
Computer Vision
Machine Learning

E-commerce Customer Segmentation — Big Data & Machine Learning

Thu, 15 Jan 2026 00:00:00 +0000

Overview

This project applies Big Data processing and machine learning to e-commerce transaction data. It builds customer-level features with RFM analysis, compares clustering approaches, and demonstrates how incoming transactions can be associated with previously computed customer segments.

The implementation uses Apache Spark and PySpark for distributed processing, Spark MLlib for K-Means clustering, and Kafka with Spark Structured Streaming for transaction enrichment.

Workflow

The project follows an end-to-end customer segmentation workflow:

JUST AND EXAMPLE : AI-Powered Phishing Detection — Training a Classifier on Email Headers

Sun, 20 Jul 2025 00:00:00 +0000

Abstract

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Methodology

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Dataset

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Feature Engineering

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Model Training

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Results

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Key Findings

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Machine-Learning on MOZA

Computer Vision Labs: OCR, Object Detection, Segmentation and Generative AI

Overview

1. OCR and Image Preprocessing

Autonomous Vehicle Perception

Overview

Technology Stack

E-commerce Customer Segmentation — Big Data & Machine Learning

Overview

Workflow

JUST AND EXAMPLE : AI-Powered Phishing Detection — Training a Classifier on Email Headers

Abstract

Methodology

Dataset

Feature Engineering

Model Training

Results

Key Findings

Conclusion