senlinuc/caffe_ocr

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1305 stars · 523 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

An experimental research project built on Caffe implementing CNN+BLSTM+CTC text recognition architectures, with modifications for LSTM, warp-ctc, multi-label data layers, and Inception/ResNet/DenseNet backbones. It includes training pipelines, synthetic Chinese/English data generation, and pretrained OCR models.

Use cases

• recognize text in images with a CNN+BLSTM+CTC model
• train a Chinese OCR model on synthetic data
• experiment with densenet and resnet OCR architectures in Caffe
• benchmark OCR accuracy and inference speed on CPU and GPU
• adapt Caffe for variable-length sequence recognition with CTC loss

When to choose

• you want to study or reproduce classic CRNN-style OCR architectures in Caffe
• you need pretrained Chinese/English OCR models with accuracy benchmarks
• you work on Windows with VS2015 and want a ready Caffe OCR setup

When to avoid

• you need a maintained production OCR system - the project is experimental and inactive since 2020
• you prefer modern frameworks like PyTorch or dedicated OCR tools like Tesseract/PaddleOCR
• you need a permissively licensed project - no license is specified

Facets

library · maturity maintenance

ocr machine-learning deep-learning image-processing computer-vision image-processing deep-learning windows cpp caffe crnn blstm ctc densenet resnet chinese-ocr text-recognition research natural-language-processing linux gpu

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