What is the difference between image processing and computer vision?
Image processing transforms images changing their appearance, quality, or format without necessarily understanding their content. Examples: resizing, cropping, noise reduction, colour correction, format conversion, background removal. Computer vision understands image content answering questions about what is depicted. Examples: "is this image a cat or a dog?", "where are the defects on this PCB?", "how many people are in this crowd?". In practice, image processing is often a preprocessing step for computer vision: raw images are cleaned, normalised, and standardised by an image processing pipeline before being fed to a computer vision model. A document image processing pipeline might deskew and denoise a scanned page (image processing) before passing it to an OCR engine (computer vision) that extracts the text.
How do you process large volumes of images efficiently?
High-throughput image processing uses a combination of parallelisation, hardware acceleration, and serverless architecture. For event-triggered processing (process each image as it is uploaded): AWS Lambda functions triggered by S3 object creation events each image processed independently, AWS Lambda scales automatically to hundreds of concurrent invocations without infrastructure management. For batch processing large existing image archives: AWS Batch (managed batch compute spin up GPU or CPU instances for duration of batch job, shut down when complete), Python multiprocessing (parallel processing on CPU cores for non-GPU workloads), and GPU acceleration via OpenCV CUDA or PyTorch transforms for processing-intensive operations (denoising, super-resolution). A well-designed pipeline can process 100,000-1,000,000 images per hour depending on processing complexity and GPU allocation.
Which OCR engine should I use Tesseract, AWS Textract, or Google Document AI?
Tesseract 5 is the best open-source OCR engine free, self-hosted (data stays on your infrastructure), good accuracy on clean printed text, and supports 100+ languages. It is the right choice when: data privacy prevents using cloud APIs, the volume is very high (cloud API costs would be prohibitive), and the document quality is good (clean, well-scanned). AWS Textract is the best managed cloud OCR for structured documents it preserves table structure, identifies form fields (label + value pairs), and handles multi-column layouts with significantly better accuracy than Tesseract on complex layouts. Use when: you are already on AWS, table and form extraction matters, and a per-page cost ($0.0015-$0.015/page) is acceptable. Google Document AI has pre-built models specifically for invoices, receipts, ID documents, and custom forms use when you have a specific document type that matches a Google pre-built model.
Can image processing pipelines handle medical images (DICOM)?
Yes, with appropriate tooling and data handling. DICOM (Digital Imaging and Communications in Medicine) is the standard format for medical imaging CT scans, MRI, X-ray, ultrasound and requires specialist handling. pydicom is the Python library for reading and writing DICOM files, extracting pixel data, and accessing DICOM metadata (patient information, acquisition parameters). MONAI (Medical Open Network for AI) is the PyTorch-based framework for medical image preprocessing and ML training analogous to torchvision but with medical imaging primitives (intensity normalisation, spatial transforms, DICOM loading). For research and development pipelines, ClickMasters builds DICOM processing systems including anonymisation (DICOM metadata de-identification to remove PHI for research compliance), format conversion, windowing (correct pixel value mapping per modality), and preprocessing for ML training. All medical data handling is scoped with the client's HIPAA or equivalent regulatory requirements.
