---
title: "Reconstructed Slides: Build & deploy AI-powered apps — Paige Bailey, Google DeepMind"
category: "slides"
video_id: "G_bHFmEAarM"
sourceLabels: ["Cropped public YouTube video frames", "Local OpenCV slide-region detection", "Local RapidOCR"]
---

# Reconstructed Slides: Build & deploy AI-powered apps — Paige Bailey, Google DeepMind

## Source Video
[Build & deploy AI-powered apps — Paige Bailey, Google DeepMind](https://www.youtube.com/watch?v=G_bHFmEAarM)

## 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/G_bHFmEAarM/slide-001.jpg]]

- Source frame: `slide-001.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `157.81`
![[assets/reconstructed-slides/G_bHFmEAarM/slide-002.jpg]]

- Source frame: `slide-002.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `176.03`
![[assets/reconstructed-slides/G_bHFmEAarM/slide-003.jpg]]

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

- Source frame: `slide-004.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `155.43`
![[assets/reconstructed-slides/G_bHFmEAarM/slide-005.jpg]]

- Source frame: `slide-005.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `153.47`
![[assets/reconstructed-slides/G_bHFmEAarM/slide-006.jpg]]

- Source frame: `slide-006.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `154.34`
![[assets/reconstructed-slides/G_bHFmEAarM/slide-007.jpg]]

- Source frame: `slide-007.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `152.92`
![[assets/reconstructed-slides/G_bHFmEAarM/slide-008.jpg]]

- Source frame: `slide-008.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `157.79`
![[assets/reconstructed-slides/G_bHFmEAarM/slide-009.jpg]]

- Source frame: `slide-009.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `156.23`
![[assets/reconstructed-slides/G_bHFmEAarM/slide-010.jpg]]

- Source frame: `slide-010.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `152.07`
![[assets/reconstructed-slides/G_bHFmEAarM/slide-011.jpg]]

- Source frame: `slide-011.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `155.53`
![[assets/reconstructed-slides/G_bHFmEAarM/slide-012.jpg]]

- Source frame: `slide-012.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `156.07`
![[assets/reconstructed-slides/G_bHFmEAarM/slide-013.jpg]]

- Source frame: `slide-013.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `156.5`
![[assets/reconstructed-slides/G_bHFmEAarM/slide-014.jpg]]

- Source frame: `slide-014.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `152.52`
![[assets/reconstructed-slides/G_bHFmEAarM/slide-015.jpg]]

- Source frame: `slide-015.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `154.88`
![[assets/reconstructed-slides/G_bHFmEAarM/slide-016.jpg]]

- Source frame: `slide-016.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `151.68`
![[assets/reconstructed-slides/G_bHFmEAarM/slide-017.jpg]]

- Source frame: `slide-018.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `149.65`
![[assets/reconstructed-slides/G_bHFmEAarM/slide-018.jpg]]

- Source frame: `slide-019.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `164.76`
![[assets/reconstructed-slides/G_bHFmEAarM/slide-019.jpg]]

- Source frame: `slide-020.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `162.44`
![[assets/reconstructed-slides/G_bHFmEAarM/slide-020.jpg]]

- Source frame: `slide-021.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `176.29`
![[assets/reconstructed-slides/G_bHFmEAarM/slide-021.jpg]]

- Source frame: `slide-023.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `154.73`
![[assets/reconstructed-slides/G_bHFmEAarM/slide-022.jpg]]

- Source frame: `slide-024.jpg`
- Crop: `full` `[0, 0, 960, 540]` score `152.28`
## Dense Scene-Detected Slide Candidates
- [[youtube-G_bHFmEAarM-dense-slides]]