CNN downsamples a 32x160 grayscale image to a sequence of 40 feature columns.
BiLSTM (2 layers) models left/right context across the sequence.
Linear + CTC predicts a character per column over 62 classes (0-9A-Za-z) plus a blank.
Greedy CTC decoding at inference.

Usage

python

import sys
from huggingface_hub import snapshot_download

repo = snapshot_download("Abulqosim0227/ocr-crnn-printed")
sys.path.insert(0, repo)
from infer import load_model, read

model, device = load_model(f"{repo}/model.safetensors")
print(read(model, device, "word.png"))

Or from a clone: python infer.py word.png

Local demo UI

A small Gradio web interface (app.py) for testing in your browser:

bash

pip install -r requirements.txt
python app.py

Open http://127.0.0.1:7860, then upload a word image or click an example from samples/.

Limitations

Reads single words / short strings up to 10 characters.
Character set is 0-9A-Za-z only — no spaces or punctuation.
Trained on synthetic printed text (DejaVu fonts). It is not trained for scene text (photos) or handwriting.
No language model, so visually ambiguous glyphs can be confused (e.g. capital I vs lowercase l). Add a language prior for production use.

Training

Data generated on the fly: random strings rendered with random fonts + light rotation/blur/noise. No external dataset.
8 epochs, Adam (lr 1e-3), CTC loss, on a single GPU in a few minutes.
Reproduce with python train.py (requires the DejaVu fonts used in dataset.py).

Files

model.safetensors weights, config.json architecture, and the full training + inference code (alphabet.py, preprocess.py, model.py, dataset.py, train.py, infer.py).