A look at 2000s text-to-speech research: how unit selection and HMM-based synthesis handled recordings, timing, pitch, and naturalness.
When 2000s Pattern Recognition Met Messy Data
How 2000s researchers moved pattern-recognition methods toward practical speech, vision, and document systems—and tested where those systems failed.
Biometrics in the 2000s: Beyond the Matching Algorithm
How 2000s biometric systems brought capture, matching, standards, testing, and human review together in access control and travel documents.
Stereo Reconstruction in the 2000s: Matches, Depth and Missing Surfaces
How 2000s stereo reconstruction methods estimated depth, handled occlusion and calibration errors, and turned disparity maps into 3D surfaces.
How Machine Learning Changed Image Analysis in the Mid-2000s
A historical look at how learned decisions entered image-analysis pipelines, and why training data, geometry, task definitions, and evaluation still mattered.
Computer Vision Research in the 2000s: Features, Geometry, and Benchmarks
How 2000s computer vision research tested object detection, image features, stereo reconstruction, tracking, and the limits of shared benchmarks.
When OCR Wasn’t Enough: Document Processing in the Mid-2000s
How mid-2000s researchers moved beyond OCR accuracy to recover page layout, reading order, tables, and searchable records from scanned documents.
How 2000s Speech Recognizers Chose Words
How HMMs, pronunciation dictionaries and language models shaped 2000s speech recognition—and why training data and recording conditions mattered.
Language Identification Before Modern AI: Text, Speech, and the Limits of a Label
How early and mid-2000s systems identified languages in text and speech, and why sample length, mixed content, uncertainty, and evaluation matter.
Machine Learning in Medical Diagnosis: From Image Findings to Clinical Evidence
A historical look at machine learning in medical diagnostics, from early image classifiers and clinical labels to validation, false alerts, and clinical use.