Models
Scene text detection models, compared.
Newest first. One table, one vocabulary: each row links to a page with the paper, the idea in plain words, results with sources, and where the code lives.
| Model | Year | Family | Output | Reported results | Speed (paper) | Code |
|---|---|---|---|---|---|---|
| PP-OCR det | 2026 | Segmentation (DB-family, mobile backbones) | Quadrilaterals per text line | none stated; see page | Tens of milliseconds per image on WebGPU (measured live in the detector's status line) | PaddlePaddle/PaddleOCR |
| FAST | 2021 | Segmentation (single-channel kernel + GPU dilation) | Polygons of arbitrary shape | Total-Text F-measure 0.816 · FAST-T at 152 FPS (abstract) | 152 FPS for FAST-T on Total-Text; over 600 FPS with TensorRT (abstract) | czczup/FAST |
| DBNet | 2020 | Segmentation (differentiable binarization) | Polygons of arbitrary shape | MSRA-TD500 F-measure 0.828 · ResNet-18 backbone at 62 FPS (abstract) ICDAR 2015 F-measure 0.873 · ResNet-50 backbone; table value, not yet checked against the PDFunverified | 62 FPS with ResNet-18 on MSRA-TD500 (abstract) | MhLiao/DB |
| CRAFT | 2019 | Segmentation (character regions + affinity) | Polygons around words, built from character-level maps | ICDAR 2015 F-measure 0.869 · table value; the abstract states no numbersunverified | — | clovaai/CRAFT-pytorch |
| EAST | 2017 | Direct regression (fully convolutional, one stage) | Rotated rectangles or quadrilaterals per word | ICDAR 2015 F-score 0.782 · at 13.2 FPS, 720p input (abstract) ICDAR 2015 F-score (best configuration) 0.807 · table value, not yet checked against the PDFunverified | 13.2 FPS at 720p resolution (abstract) | argman/EAST |
| CTPN | 2016 | Proposal-based (vertical anchors + recurrent connection) | Horizontal text lines | ICDAR 2013 F-measure 0.88 · abstract ICDAR 2015 F-measure 0.61 · abstract | 0.14 s per image with a VGG-16 backbone on a 2016 GPU (abstract) | Original Caffe implementation |
How to read this table
Results are the papers' own claims. A value marked unverified was quoted from a results table and has not yet been checked against the PDF; values without the mark come from the abstract. Blank beats wrong, so some rows are sparse.
Datasets differ. ICDAR 2013 is easy, ICDAR 2015 is hard, Total-Text and CTW1500 are curved, MSRA-TD500 is line-level. A 0.83 on one and a 0.61 on another can be the same quality of detector. The datasets pages explain what each one tests.
Speed is not comparable. GPUs, input sizes and what counts as "inference" (with or without post-processing) changed between 2016 and 2021. Treat the speed column as each paper's headline, not a benchmark.
Families in one line each
Proposal-based: anchors along a line, linked into text (CTPN).
Direct regression: geometry predicted per pixel, merged by NMS (EAST).
Segmentation: a per-pixel text map turned into shapes (CRAFT, DBNet, FAST, PP-OCR).