Computer Vision — & OCR Modules
Image Processing, Facial Feature Detection & Optical Character Recognition in Python
Project Overview
Automated document parsing and real-time biometric tracking rely on robust digital image manipulation pipelines before machine learning models can accurately interpret visual features.
This project represents a collection of practical Python computer vision and OCR implementations. It explores digital image preprocessing, contour segmentation, adaptive thresholding, and facial landmark feature extraction using OpenCV, NumPy, and optical recognition engines.
The Engineering Problem
Raw imagery from budget webcams and scanned office paperwork poses severe hurdles for recognition software:
- Uneven illumination, glare, and background shadows create false edges that confuse OCR classification.
- Document skew and slight rotational tilts break linear line-by-line bounding box isolation.
- Noise artifacts and compression degradation reduce character edge sharpness.
- Real-time video processing loops must maintain low per-frame latency to avoid lag.
The Solution Built
Developed reusable Python image processing pipelines. Implemented adaptive thresholding routines (including Otsu's binarization), morphological operations (opening, closing, dilation) to bridge broken strokes, and contour-based bounding box clustering. For facial analysis, utilized feature vector extraction and landmark alignment across video frame buffers.
System Architecture
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Preprocessing Pipeline (Grayscale • Bilateral Denoising • Otsu Adaptive Thresholding)
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Morphological Operations & Contour Bounding Box Extraction
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Recognition Engine (Tesseract OCR Engine • Facial Landmark Feature Vectors)
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Structured Data Output (Normalized Text Strings • Detection Confidence Scores)
Key Capabilities
- Document OCR Pipeline: Automated grayscale conversion, adaptive binarization, and noise suppression yielding clean text output from physical papers.
- Facial Landmark Prototypes: Real-time feature detection and bounding box visualization from live webcam video frames.
- Advanced Morphological Filtering: Morphological top-hat and gradient transforms to isolate character strokes from textured backgrounds.
- NumPy Vectorized Matrix Operations: Fast pixel transformations avoiding slow Python loops for high throughput.
- Deskewing Subsystem: Evaluates minimum area bounding boxes around text contours to compute document tilt angle and rotate frames back to horizontal alignment.