stvir/pmtd — explained in plain English
Analysis updated 2026-08-08 · repo last pushed 2019-08-02
Build a document-scanning app that locates text before reading it.
Detect street signs from car camera images for navigation systems.
Extract text regions from product photos for catalog organization.
Evaluate text detection accuracy on academic benchmark datasets.
| stvir/pmtd | dev-ocr/polymarket-arbitrage-trading-bot | freertos/freertos-plus-tcp | |
|---|---|---|---|
| Stars | 213 | 213 | 213 |
| Language | — | TypeScript | C |
| Last pushed | 2019-08-02 | — | 2026-07-10 |
| Maintenance | Dormant | — | Active |
| Setup difficulty | moderate | hard | hard |
| Complexity | 3/5 | 4/5 | 4/5 |
| Audience | researcher | developer | developer |
Figures from each repo's GitHub metadata at analysis time.
Requires PyTorch and an object detection framework, inference-only so no training infrastructure needed but environment setup is non-trivial.
PMTD (Pyramid Mask Text Detector) is a tool that automatically finds and outlines text in images. Built by SenseTime's Video Intelligence Research team, it is designed to locate words in pictures, whether they are printed on signs, documents, or captured in the wild. Instead of a human manually drawing boxes around text, this software does it automatically. The system works by analyzing images at multiple scales simultaneously, which helps it spot text of varying sizes. It uses a technique that combines bounding boxes with more precise mask shapes, allowing it to follow the actual contour of irregularly angled or curved text rather than forcing everything into a flat rectangle. The project provides pre-trained models that have already learned from two standard text-detection benchmark datasets (ICDAR 2015 and ICDAR 2017 MLT), so users can run it on new images right away without needing to train it themselves. This would be useful for anyone building applications that involve reading or organizing text from images. For example, someone developing a document-scanning app, a system that reads street signs from car cameras, or a tool that extracts text from product photos could use this as the first step, finding where the text actually is before a separate tool reads what the text says. One thing worth noting is that the project provides inference code only, meaning it is set up for running the detector on images rather than training new models from scratch. It is built on top of an existing open-source object detection framework, and the results it produces match what the researchers reported in their original paper. The output can be formatted for submission to academic benchmarks, suggesting it is oriented toward research and evaluation use cases.
Automatically finds and outlines text in images at multiple scales, using mask shapes to follow curved or angled text. Comes with pre-trained models ready to run on new images.
Dormant — no commits in 2+ years (last push 2019-08-02).
No license information provided in the repository, so usage rights are unclear.
Setup difficulty is rated moderate, with roughly 30min to a first successful run.
Mainly researcher.
This repo across BitVibe Labs
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