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Add dataset card for SceneText50

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This PR adds a dataset card for SceneText50, including the `object-detection` task category, relevant tags, and links to the paper and code repository. It also documents the dataset structure and annotation format, and provides download instructions from the GitHub README.

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  1. README.md +53 -0
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  ---
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  license: apache-2.0
 
 
 
 
 
 
 
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  ---
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  license: apache-2.0
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+ task_categories:
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+ - object-detection
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+ tags:
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+ - scene-text
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+ - text-detection
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+ - video
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+ - multilingual
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  ---
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+
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+ # SceneText50
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+
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+ SceneText50 is a diverse multilingual outdoor dataset introduced in the paper [Back-Tracking from Clarity: Self-Learning to See Text from Afar](https://huggingface.co/papers/2609.32477).
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+
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+ It consists of 50 outdoor ego-centric videos (~840 clips) from 17 countries, with quadrilateral text annotations. The annotations are for **evaluation**; the self-learning pipeline introduced in the paper needs no manual labels for training.
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+
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+ Code: https://github.com/trid2912/BackTrackingText
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+
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+ ## Dataset structure
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+
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+ ```text
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+ tracking_data/
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+ β”œβ”€β”€ 01/ 02/ ... 50/ # one folder per video
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+ β”‚ β”œβ”€β”€ clip_10/ # a clip = a short frame sequence
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+ β”‚ β”‚ β”œβ”€β”€ clip_10_0000.jpg
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+ β”‚ β”‚ β”œβ”€β”€ clip_10_0001.jpg
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+ β”‚ β”‚ └── ...
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+ β”‚ β”œβ”€β”€ clip_10_0000.txt # per-frame annotation (same basename as the frame)
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+ β”‚ β”œβ”€β”€ clip_10_0001.txt
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+ β”‚ └── ...
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+ ```
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+
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+ Each annotation line is one text instance:
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+
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+ ```text
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+ instance_id x1 y1 x2 y2 x3 y3 x4 y4
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+ ```
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+
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+ `instance_id` is the track ID (consistent across frames of a clip) and the four `(x, y)` pairs are the clockwise quadrilateral corners.
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+
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+ ## Sample usage
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+
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+ Download from Hugging Face:
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+
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+ ```bash
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+ # option 1 β€” huggingface-cli
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+ pip install -U "huggingface_hub[cli]"
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+ huggingface-cli download Tri1/SceneText50 --repo-type dataset --local-dir data/tracking_data
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+
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+ # option 2 β€” git + git-lfs
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+ git lfs install
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+ git clone https://huggingface.co/datasets/Tri1/SceneText50 data/tracking_data
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+ ```
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+
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+ Point `--image_root` at this `tracking_data` folder when running the tracker / pipeline.