bgshih/crnn
Convolutional Recurrent Neural Network (CRNN) for image-based sequence recognition. observed · 2026-08-28
Health v2 · maintenance only
32/100
- Activity 0
- Release rhythm 35
- Longevity 100
Flags: no_releases
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 3910
- days_rel: n/a
- days_push: 2739
- n_releases_24m: 0
Adoption not part of the score
2105 stars · 547 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
An implementation of the Convolutional Recurrent Neural Network (CRNN), combining CNN, RNN, and CTC loss for image-based sequence recognition such as scene text recognition and OCR. It is built on Torch7 with Lua and includes pretrained models, a demo, and training tooling.
Use cases
- recognize text in scene images
- build an OCR model for word images
- train a CRNN model on my own dataset
- lexicon-free text recognition from images
- implement CTC-based sequence recognition
When to choose
- you need the reference CRNN implementation for scene text recognition
- you want to train a CTC-based text recognizer on custom data
- you are working in Torch7/Lua
When to avoid
- you need a maintained PyTorch or TensorFlow OCR solution
- you require end-to-end text detection plus recognition
- your environment is not Linux with CUDA GPUs
Facets
library · maturity maintenance
ocr machine-learning deep-learning image-processing computer-vision machine-learning deep-learning lua torch7 ctc-loss scene-text-recognition sequence-recognition cnn-rnn natural-language-processing linux gpu docker
1 source
- readme: https://github.com/bgshih/crnn · fetched 2026-08-28 · 3d9320da5b3d
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| bgshih/crnn | main | 32 |
For agents
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem