Recognizing Chinese characters the old-fashioned CNN-RNN way

What it does This repository implements a convolutional recurrent network (CRNN) in PyTorch for recognizing Chinese characters in images. It pairs CNN feature extraction with recurrent layers and CTC loss, which lets the model learn from line-level labels instead of painstaking per-character bounding boxes. You can train on the synthetic 360CC dataset or bring your own image-text pairs.

The interesting bit The project confronts a scale problem that Latin OCR rarely faces: a character set numbering in the thousands. The README casually drops a char_std_5990.txt file, implying support for just under six thousand glyphs, yet treats the whole affair as a standard PyTorch training run with YAML configs.

Key highlights

• Targets a ~5,990-character Chinese glyph space using CTC decoding
• Supports both the synthetic 360CC dataset and custom data via YAML config files
• Includes a pretrained checkpoint for immediate demo.py inference
• Fixed-length training works out of the box; variable length requires dataloader edits
• Logs training curves through tensorboardX

Caveats

• Development environment targets PyTorch 1.2.0 and CUDA 10.0, so modern dependency stacks may need massaging
• Random-length training is noted as possible but requires modifying the dataloader yourself
• Dataset and label downloads are hosted on Baidu Pan, which may be region-locked or unstable

Verdict Useful as a starter template or baseline if you are building Chinese text recognition into a larger pipeline and can tolerate some dependency archaeology. Not the right fit if you need a drop-in, multilingual OCR engine with zero tuning.