mlsanityData to 14 Oct 2026

ScreenSeg: On-Device Screenshot Layout Analysis

arXiv 2104.08052 ↗First posted 16 Apr 2021cs.CVcited by 0

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Abstract (arXiv)

We propose a novel end-to-end solution that performs a Hierarchical Layout Analysis of screenshots and document images on resource constrained devices like mobilephones. Our approach segments entities like Grid, Image, Text and Icon blocks occurring in a screenshot. We provide an option for smart editing by auto highlighting these entities for saving or sharing. Further this multi-level layout analysis of screenshots has many use cases including content extraction, keyword-based image search, style transfer, etc. We have addressed the limitations of known baseline approaches, supported a wide variety of semantically complex screenshots, and developed an approach which is highly optimized for on-device deployment. In addition, we present a novel weighted NMS technique for filtering object proposals. We achieve an average precision of about 0.95 with a latency of around 200ms on Samsung Galaxy S10 Device for a screenshot of 1080p resolution. The solution pipeline is already commercialized in Samsung Device applications i.e. Samsung Capture, Smart Crop, My Filter in Camera Application, Bixby Touch.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 25 references that are in the map. First posted on arXiv 16 Apr 2021.

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9 of 25 references.

References · 9

  1. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  2. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
  3. 2017Rethinking Atrous Convolution for Semantic Image Segmentationcited by 2,161
  4. 2016SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model sizecited by 1,060
  5. 2016Synthetic Data for Text Localisation in Natural Imagescited by 396
  6. 2017Soft-NMS -- Improving Object Detection With One Line of Codecited by 382
  7. 2019Character Region Awareness for Text Detectioncited by 278
  8. 2019ICDAR2019 Robust Reading Challenge on Multi-lingual Scene Text Detection and Recognition -- RRC-MLT-2019cited by 139
  9. 2016SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Drivingcited by 55

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