alphabet-classifier

Handwritten Character Classifier (EMNIST, MobileNetV2)

Two MobileNetV2-based ONNX models for classifying grayscale images of handwritten characters. Both models predict case-insensitively — they are trained on both upper and lowercase handwriting but always output a single canonical label per letter.

Use the alphabet model when your input is guaranteed to be a letter (higher accuracy, no digit/letter confusion). Use the alphanumeric model when the input may be a digit or a letter.

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Model Details

Shared architecture

Alphanumeric model — 37 classes

Alphabet model — 27 classes

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Training Configuration

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Per-class Performance

Alphanumeric model

Digits

Letters

Blank

Hardest cases are I (F1=0.607) and L (F1=0.554), both confused with digit 1; and O (F1=0.671), confused with digit 0. These are inherent digit–letter ambiguities in alphanumeric OCR.

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Alphabet model

Hardest cases are I (F1=0.761) and L (F1=0.768), which are visually similar across handwriting styles. All other letters achieve F1 ≥ 0.90, and blank is perfect.

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Usage

Alphanumeric model

from huggingface_hub import hf_hub_download
import onnxruntime as ort
import numpy as np
from PIL import Image

CHAR_CLASSES = list("0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ") + ["blank"]

path = hf_hub_download(
    repo_id="hermitkk/alphabet-classifier",
    filename="outputs/exports/alphanumeric_model.onnx",
)
session = ort.InferenceSession(path)

# Preprocess a 96x96 grayscale crop
img = Image.open("character.png").convert("L").resize((96, 96))
x = (np.array(img, dtype=np.float32) / 255.0 - 0.5) / 0.5
x = x[np.newaxis, np.newaxis, :, :]  # (1, 1, 96, 96)

logits = session.run(None, {"input": x})[0]
pred = int(np.argmax(logits))
print(CHAR_CLASSES[pred])  # e.g. "A", "3", "blank"

Alphabet model

from huggingface_hub import hf_hub_download
import onnxruntime as ort
import numpy as np
from PIL import Image

ALPHA_CLASSES = list("ABCDEFGHIJKLMNOPQRSTUVWXYZ") + ["blank"]

path = hf_hub_download(
    repo_id="hermitkk/alphabet-classifier",
    filename="outputs/exports/alphabet_model.onnx",
)
session = ort.InferenceSession(path)

# Preprocess a 96x96 grayscale crop
img = Image.open("letter.png").convert("L").resize((96, 96))
x = (np.array(img, dtype=np.float32) / 255.0 - 0.5) / 0.5
x = x[np.newaxis, np.newaxis, :, :]  # (1, 1, 96, 96)

logits = session.run(None, {"input": x})[0]
pred = int(np.argmax(logits))
print(ALPHA_CLASSES[pred])  # e.g. "A", "blank"

Note: both models accept 96 × 96 single-channel float32 input, normalized to mean 0.5 / std 0.5. White pixels (blank paper) map to +1.0 and dark ink maps toward −1.0.

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Reproduce Training

git clone https://huggingface.co/hermitkk/alphabet-classifier
cd alphabet-classifier
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt

# Train alphanumeric model
python main.py train --config config/config.yaml

# Train alphabet model
python main.py train --config config/config_alphabet.yaml

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License

MIT