---
title: "Reconstructed Slides: New York Times' Connections: A Case Study on NLP in Word Games — Shafik Quoraishee, NYT Games"
category: "slides"
video_id: "P_uhFGH4J9Y"
sourceLabels: ["Cropped public YouTube video frames", "Local OpenCV slide-region detection", "Local RapidOCR"]
---

# Reconstructed Slides: New York Times' Connections: A Case Study on NLP in Word Games — Shafik Quoraishee, NYT Games

## Source Video
[New York Times' Connections: A Case Study on NLP in Word Games — Shafik Quoraishee, NYT Games](https://www.youtube.com/watch?v=P_uhFGH4J9Y)

## Method
This deck is reconstructed from the existing video frame captures by detecting likely slide regions with OpenCV, cropping/upscaling those regions, deduplicating similar crops, and OCRing the cropped slide images locally. It is a cleaner companion to the full-stage frame deck.

## Reconstructed Slides
![[assets/reconstructed-slides/P_uhFGH4J9Y/slide-001.jpg]]

- Source frame: `slide-001.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `160.13`
![[assets/reconstructed-slides/P_uhFGH4J9Y/slide-002.jpg]]

- Source frame: `slide-002.jpg`
- Crop: `contour` `[0, 0, 960, 540]` score `176.55`
![[assets/reconstructed-slides/P_uhFGH4J9Y/slide-003.jpg]]

- Source frame: `slide-003.jpg`
- Crop: `contour` `[0, 0, 960, 540]` score `176.17`
![[assets/reconstructed-slides/P_uhFGH4J9Y/slide-004.jpg]]

- Source frame: `slide-004.jpg`
- Crop: `contour` `[0, 0, 960, 540]` score `176.91`
![[assets/reconstructed-slides/P_uhFGH4J9Y/slide-005.jpg]]

- Source frame: `slide-005.jpg`
- Crop: `contour` `[0, 0, 960, 540]` score `176.61`
![[assets/reconstructed-slides/P_uhFGH4J9Y/slide-006.jpg]]

- Source frame: `slide-006.jpg`
- Crop: `contour` `[0, 0, 960, 540]` score `176.5`
## Dense Scene-Detected Slide Candidates
- [[youtube-P_uhFGH4J9Y-dense-slides]]