ModelPublicadoExterno

Handwritten Digits Classification Cnn Transformer

Based on: "Handwriting Recognition through Neural Networks: Enhancing Accuracy and Performance" (Rajest & Regin, 2024)

Fuentehuggingface.co/leobottaro/handwritten-digits-classification-cnn-transformer/tree/aff5e6f474d0f4129e55e91feea724e28532723f

TipoOtherVersiónRevision aff5e6f

Fuente del modelo

Tipo: Other
Tamaño del archivo: 9,77–29,1 MB
Versión: vRevision aff5e6f
Actualizado: 16 sept 2026
Descargas: 0
Fuente: Hugging Face

Descripción de la fuente

Handwritten Digit Classification — CNN + Transformer Hybrid

Based on: "Handwriting Recognition through Neural Networks: Enhancing Accuracy and Performance" (Rajest & Regin, 2024)

Source code: github.com/leocb/handwritten-digits-cnn-transformer

Model Description

Hybrid CNN + Transformer architecture for handwritten digit classification (0-9). CNN extracts low-level features (edges, shapes); Transformer encoder captures spatial dependencies via self-attention. Trained with Apple Metal (MPS) GPU acceleration.

Input: 28×28 grayscale image (white background, dark digit), shape (1, 28, 28), float32, range [0, 1].

Output: 10-class logits (digits 0-9).

Architecture

Component	Layers	Output Shape
Conv Block 1	Conv(1→32) → BN → ReLU → Pool/2	(32, 14, 14)
Conv Block 2	Conv(32→64) → BN → ReLU → Pool/2	(64, 7, 7)
Conv Block 3	Conv(64→128) → BN → ReLU	(128, 7, 7)
Conv Block 4	Conv(128→256) → BN → ReLU → Pool/2	(256, 3, 3)
Flatten + Positional Encoding	9×256 learned position embeddings	(9, 256)
Transformer Encoder	4 layers, 8 heads, d=256, ff=512, GELU, Pre-LN	(9, 256)
Classification Head	Mean → LayerNorm → FC(128) → ReLU → Dropout → FC(10)	(10,)

Parameters: 2,534,218 (all trainable)

Performance

Evaluated on held-out validation set (36,758 samples from 3 combined datasets):

Metric	Value
Accuracy	99.93%
Precision (macro)	0.9993
Recall (macro)	0.9993
F1 (macro)	0.9993

Per-Class

Digit	Precision	Recall	F1
0	0.9997	0.9997	0.9997
1	0.9995	0.9984	0.9989
2	0.9995	0.9992	0.9993
3	0.9992	0.9997	0.9995
4	0.9989	0.9995	0.9992
5	0.9997	0.9995	0.9996
6	0.9986	0.9997	0.9992
7	0.9995	0.9997	0.9996
8	0.9992	0.9986	0.9989
9	0.9995	0.9992	0.9993

Training Data

Combined from 3 datasets (503,100 train + 36,758 validation, all 28×28 grayscale):

EMNIST Digits (280,000) — NIST EMNIST
HWD-V1 (136,920 + 15,218) — Kaggle HWD-V1 (standard + edge cases)
Handwritten Digits (86,180 + 21,540) — nguyenminh4099/handwritten-digits

Preprocessed dataset available at leobottaro/handwritten-digits-pack.

Training

Parameter	Value
Optimizer	AdamW (lr=1e-4, wd=1e-4)
Scheduler	ReduceLROnPlateau (factor=0.5, patience=5)
Loss	Categorical Cross-Entropy
Batch Size	256
Epochs	22 (best), early stopped at 34
Mixed Precision	MPS AMP
Hardware	Apple M-series (MPS)

Usage

PyTorch

import torch

# Load model
ckpt = torch.load("model_best.pt", weights_only=True)
model = CNNTransformer(num_classes=10)
model.load_state_dict(ckpt["model_state_dict"])
model.eval()

# Inference
with torch.no_grad():
    logits = model(image_tensor)  # (1, 1, 28, 28) → (1, 10)
    pred = logits.argmax(1).item()

ONNX Runtime

import onnxruntime as ort
import numpy as np

session = ort.InferenceSession("model.onnx")
logits = session.run(None, {"input": image_array})[0]  # (1, 1, 28, 28) → (1, 10)
pred = logits.argmax(axis=1)[0]

Files

model_best.pt — PyTorch checkpoint (weights + optimizer state + history)
model.onnx — ONNX model (opset 17, dynamic batch)

Citation

@article{rajest2024handwriting,
  title={Handwriting Recognition through Neural Networks: Enhancing Accuracy and Performance},
  author={Rajest, S. Suman and Regin, R.},
  journal={Central Asian Journal of Medical and Natural Science},
  volume={5},
  number={4},
  pages={1010--1024},
  year={2024}
}

License

MIT

Fuentes

1 fuente

Hugging FacePrincipalVersión aff5e6f474d0f4129e55e91feea724e28532723f

huggingface.co/leobottaro/handwritten-digits-classification-cnn-transformer/tree/aff5e6f474d0f4129e55e91feea724e28532723f

Verificado 16 sept

Artefactos del modelo

2 artefactos

model_best.pt

pt · 29,1 MB · SHA-256 21de3222b13f…d1bf · Hugging Face

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model.onnx

onnx · 9,77 MB · SHA-256 8b723398c946…7c27 · Hugging Face

Descargar

Extractos de fuentes

2 extractos

Readme16 septhuggingface.co/leobottaro/handwritten-digits-classification-cnn-transformer

--- language: en license: mit tags: - handwritten-digit-recognition - cnn - transformer - pytorch - onnx - classification - mnist - emnist datasets: - emnist - metricasecuador/hwd-v1 - nguyenminh4099/handwritten-digits metrics: - accuracy - precision - recall - f1 pipeline_tag: image-classification model-index: - name: CNN-Transformer Handwritten Digit Classifier results: - task: type: image-classification dataset: name: Combined Handwritten Digits type: handwritten-digits-classification metrics: - type: accuracy value: 0.9993 - type: precision value: 0.9993 - type: recall value: 0.9993 - type: f1 value: 0.9993 --- # Handwritten Digit Classification — CNN + Transformer Hybrid Based on: *"Handwriting Recognition through Neural Networks: Enhancing Accuracy and Performance"* (Rajest & Regin, 2024) **Source code**: [github.com/leocb/handwritten-digits-cnn-transformer](https://github.com/leocb/handwritten-digits-cnn-transformer) ## Model Description Hybrid CNN + Transformer architecture for handwritten digit classification (0-9). CNN extracts low-level features (edges, shapes); Transformer encoder captures spatial dependencies via self-attention. Trained with Apple Metal (MPS) GPU acceleration. **Input**: 28×28 grayscale image (white background, dark digit), shape `(1, 28, 28)`, float32, range [0, 1]. **Output**: 10-class logits (digits 0-9). ## Architecture | Component | Layers | Output Shape | |---|---|---| | Conv Block 1 | Conv(1→32) → BN → ReLU → Pool/2 | (32, 14, 14) | | Conv Block 2 | Conv(32→64) → BN → ReLU → Pool/2 | (64, 7, 7) | | Conv Block 3 | Conv(64→128) → BN → ReLU | (128, 7, 7) | | Conv Block 4 | Conv(128→256) → BN → ReLU → Pool/2 | (256, 3, 3) | | Flatten + Positional Encoding | 9×256 learned position embeddings | (9, 256) | | Transformer Encoder | 4 layers, 8 heads, d=256, ff=512, GELU, Pre-LN | (9, 256) | | Classification Head | Mean → LayerNorm → FC(128) → ReLU → Dropout → FC(10) | (10,) | **Parameters**: 2,534,218 (all trainable) ## Performance Evaluated on held-out validation set (36,758 samples from 3 combined datasets): | Metric | Value | |---|---| | Accuracy | **99.93%** | | Precision (macro) | 0.9993 | | Recall (macro) | 0.9993 | | F1 (macro) | 0.9993 | ### Per-Class | Digit | Precision | Recall | F1 | |---|---|---|---| | 0 | 0.9997 | 0.9997 | 0.9997 | | 1 | 0.9995 | 0.9984 | 0.9989 | | 2 |...

Note16 septhuggingface.co/leobottaro/handwritten-digits-classification-cnn-transformer

Source context: 0 downloads · 0 likes · Pipeline image-classification · Repo leobottaro/handwritten-digits-classification-cnn-transformer