Handwritten Panjabi Digit Recognition

Recognises one handwritten Gurmukhi digit (੦ ੧ ੨ ੩ ੪ ੫ ੬ ੭ ੮ ੯) in an image. It is the model behind the digit recognition on AI Panjab.

Try it without code: the Space: draw a digit and get the top three guesses. The Space also has a Use via API page (Python, JavaScript, curl).

Files

File	What it is
`model.onnx`	The model for ONNX Runtime (CPU is enough). Use this one.
`labels.json`	The ten labels, in the order of the model's outputs.
`inference.py`	The code that runs the model: picture in, top guesses out (the loader uses it).
`requirements.txt`	What `inference.py` needs: `onnxruntime`, `numpy`, `pillow`.
`model.keras`	The same model for training, in Keras 3 (PyTorch backend): to fine-tune or continue training.
`training.py`	The code that trains it: images in, a new model folder (`model.keras` + `model.onnx`) out.
`requirements-train.txt`	What training needs: adds `torch`, `keras`, `onnx`, `datasets`.
`config.json`	The model's description, and its training dataset.

Input and output

Input image: float32, shape (batch, 28, 28, 1), grayscale with values 0 to 255 (the model rescales them itself). Dark ink on a light background, one digit filling most of the picture.
Output: ten probabilities, one per label in labels.json.

To turn a picture into the input exactly as the model was trained:

a transparent background (RGBA) is put on white; any other image is converted to RGB;
grayscale with the ITU-R 601 weights: 0.2989 R + 0.5870 G + 0.1140 B, kept in floating point;
resized to 28 x 28 with bilinear (antialiased) resizing.

Use it

pip install git+https://huggingface.co/aipanjab/model-loader

from aipanjab import load

model = load("digit-recognition")
model.predict("my-digit.png")    # {'੨': 0.99, '੩': 0.01, '੭': 0.0}

The model is downloaded the first time and cached after that; it runs on your CPU, or on an NVIDIA GPU when onnxruntime-gpu is installed (load("digit-recognition", device="cpu") or "cuda" to choose).

predict takes a file path, an open file or a PIL image, and top_k (default 3). A picture with nothing drawn on it raises an error.

Train it

Continue training this model on your own handwriting, or on this model's dataset:

pip install "aipanjab-loader[train] @ git+https://huggingface.co/aipanjab/model-loader"

from aipanjab import load

model = load("digit-recognition").train(data="my-digits", epochs=10)   # my-digits/੨/1.png, my-digits/੨/2.png, ...
model.predict("my-digit.png")
model.save("my-digit-model")                                          # later: load("my-digit-model")

data is a folder with one folder per digit (named ੦ … ੯), a Hugging Face dataset id with image and label columns, or a list of (image, label) pairs; without data, the model's own dataset. Other options: batch_size (32), lr (0.0001), test_size (0.1, the share of images kept to check the model when the data has no test split). Training uses a GPU when there is one (device="cpu", "cuda" or "mps" to choose). The new model comes with both model.keras and model.onnx.

Training data

aipanjab/handwritten-digits: digits drawn by people on the AI Panjab platform (3,533 for training, 884 for testing), drawn with a black pen on a canvas of about 350 x 350. The model works best on pictures like these: one digit, dark on light, not too small in the picture.

Training procedure

Two convolutions (128 filters, 3 x 3, GELU then ReLU, each followed by 3 x 3 max pooling), dense layers of 128 and 64 (tanh) and a softmax over the 10 digits; Adam (learning rate 0.001), sparse categorical cross-entropy, 50 epochs, keeping the best epoch on the test split. Accuracy on the 884 test images: 98.6%. model.onnx and model.keras hold the same weights and give the same answers (outputs differ by less than 0.000002).

aipanjab
/

digit-recognition