ModelPublicadoExterno

Alphabet Classifier

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...

Fuentehuggingface.co/hermitkk/alphabet-classifier/tree/cf2bb20a6606fa405afb8f0b3df986c22169760f

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Fuente del modelo

Tipo: Other
Tamaño del archivo: 153 KB–9,74 MB
Versión: vRevision cf2bb20
Actualizado: 22 oct 2026
Descargas: 0
Fuente: Hugging Face

Descripción de la fuente

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.

Model	Classes	Val Accuracy	ONNX
Alphanumeric	Digits 0–9 + Letters A–Z + blank (37 total)	91.42%	`outputs/exports/alphanumeric_model.onnx`
Alphabet	Letters A–Z + blank (27 total)	96.18%	`outputs/exports/alphabet_model.onnx`

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.

Model Details

Shared architecture

Property	Value
Backbone	MobileNetV2 (pretrained ImageNet)
Head	Linear(1280→256) → BatchNorm → ReLU → Dropout(0.3) → Linear(256→N)
Parameters	~2.56M
Input	96 × 96 grayscale (expanded to 3-channel internally)
Format	ONNX (opset 17)

Alphanumeric model — 37 classes

Property	Value
Classes	`0`–`9` (indices 0–9), `A`–`Z` (indices 10–35), `blank` (index 36)
Dataset	EMNIST byclass — both upper and lowercase handwriting, labels folded to uppercase
Train samples	711,932
Val samples	118,323
Val accuracy	91.42%
Macro avg F1	0.917

Alphabet model — 27 classes

Property	Value
Classes	`A`–`Z` (indices 0–25), `blank` (index 26)
Dataset	EMNIST letters — upper and lowercase handwriting already merged at source
Train samples	138,800
Val samples	22,800
Val accuracy	96.18%
Macro avg F1	0.960

Training Configuration

Setting	Value
Optimizer	AdamW — backbone lr × 0.1, head lr 5e-4, weight_decay 1e-4
Scheduler	LinearLR warmup (5 epochs) → CosineAnnealingLR
Loss	CrossEntropyLoss with inverse-frequency class weights + label smoothing 0.10
Batch size	256
Max epochs	50
Early stopping	patience 10
Augmentation	RandomAffine, RandomPerspective, ColorJitter, GaussianBlur, RandomErasing
Mixed precision	AMP (CUDA only)
Blank class	Synthetic white images; hard-floored weight ≥ 3.0

Per-class Performance

Alphanumeric model

Digits

Class	Precision	Recall	F1
0	0.741	0.686	0.712
1	0.748	0.693	0.719
2	0.984	0.949	0.967
3	0.997	0.995	0.996
4	0.989	0.974	0.981
5	0.985	0.928	0.956
6	0.987	0.976	0.981
7	0.994	0.997	0.995
8	0.994	0.988	0.991
9	0.953	0.962	0.958

Letters

Class	Precision	Recall	F1
A	0.977	0.969	0.973
B	0.914	0.971	0.942
C	0.966	0.985	0.975
D	0.961	0.976	0.969
E	0.992	0.989	0.990
F	0.985	0.983	0.984
G	0.818	0.821	0.820
H	0.967	0.982	0.974
I	0.545	0.684	0.607
J	0.930	0.950	0.940
K	0.987	0.993	0.990
L	0.573	0.537	0.554
M	0.992	0.998	0.995
N	0.985	0.984	0.984
O	0.648	0.696	0.671
P	0.986	0.994	0.990
Q	0.783	0.761	0.772
R	0.986	0.982	0.984
S	0.913	0.978	0.944
T	0.989	0.989	0.989
U	0.969	0.955	0.961
V	0.912	0.956	0.934
W	0.988	0.997	0.992
X	0.976	0.990	0.983
Y	0.896	0.946	0.920
Z	0.762	0.921	0.834

Blank

Class	Precision	Recall	F1
blank	1.000	1.000	1.000

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.

Alphabet model

Class	Precision	Recall	F1
A	0.959	0.976	0.968
B	0.996	0.986	0.991
C	0.984	0.979	0.981
D	0.976	0.974	0.975
E	0.984	0.986	0.985
F	0.994	0.976	0.985
G	0.927	0.874	0.900
H	0.976	0.978	0.977
I	0.760	0.761	0.761
J	0.974	0.965	0.969
K	0.996	0.995	0.996
L	0.765	0.770	0.768
M	0.986	0.999	0.993
N	0.980	0.981	0.981
O	0.975	0.980	0.978
P	0.991	0.991	0.991
Q	0.890	0.928	0.908
R	0.979	0.978	0.978
S	0.992	0.989	0.991
T	0.975	0.988	0.981
U	0.961	0.944	0.952
V	0.945	0.958	0.951
W	0.997	0.990	0.993
X	0.990	0.991	0.991
Y	0.965	0.978	0.971
Z	0.995	0.998	0.996
blank	1.000	1.000	1.000

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.

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.

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

License

MIT

Fuentes

1 fuente

Hugging FacePrincipalVersión cf2bb20a6606fa405afb8f0b3df986c22169760f

huggingface.co/hermitkk/alphabet-classifier/tree/cf2bb20a6606fa405afb8f0b3df986c22169760f

Verificado 22 oct

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Artefactos del modelo

2 artefactos

outputs/exports/alphabet_model.onnx

onnx · 9,74 MB · SHA-256 a0c968787561…8688 · Hugging Face

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outputs/exports/alphanumeric_model.onnx

onnx · 153 KB · SHA-256 8275b7a63cd7…85ad · Hugging Face

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Extractos de fuentes

3 extractos

Readme22 octhuggingface.co/hermitkk/alphabet-classifier

--- license: mit tags: - image-classification - onnx - pytorch - letters - digits - alphanumeric - emnist datasets: - emnist metrics: - accuracy --- # 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. | Model | Classes | Val Accuracy | ONNX | |---|---|---|---| | **Alphanumeric** | Digits 0–9 + Letters A–Z + blank (37 total) | **91.42%** | `outputs/exports/alphanumeric_model.onnx` | | **Alphabet** | Letters A–Z + blank (27 total) | **96.18%** | `outputs/exports/alphabet_model.onnx` | 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. --- ## Model Details ### Shared architecture | Property | Value | |---|---| | Backbone | MobileNetV2 (pretrained ImageNet) | | Head | Linear(1280→256) → BatchNorm → ReLU → Dropout(0.3) → Linear(256→*N*) | | Parameters | ~2.56M | | Input | 96 × 96 grayscale (expanded to 3-channel internally) | | Format | ONNX (opset 17) | ### Alphanumeric model — 37 classes | Property | Value | |---|---| | Classes | `0`–`9` (indices 0–9), `A`–`Z` (indices 10–35), `blank` (index 36) | | Dataset | EMNIST byclass — both upper and lowercase handwriting, labels folded to uppercase | | Train samples | 711,932 | | Val samples | 118,323 | | Val accuracy | **91.42%** | | Macro avg F1 | 0.917 | ### Alphabet model — 27 classes | Property | Value | |---|---| | Classes | `A`–`Z` (indices 0–25), `blank` (index 26) | | Dataset | EMNIST letters — upper and lowercase handwriting already merged at source | | Train samples | 138,800 | | Val samples | 22,800 | | Val accuracy | **96.18%** | | Macro avg F1 | 0.960 | --- ## Training Configuration | Setting | Value | |---|---| | Optimizer | AdamW — backbone lr × 0.1, head lr 5e-4, weight_decay 1e-4 | | Scheduler | LinearLR warmup (5 epochs) → CosineAnnealingLR | | Loss | CrossEntropyLoss with inverse-frequency class weights + label smoothing 0.10 | | Batch size | 256 | | Max epochs | 50 | | Early stopping | patience 10 | | Augmentation | RandomAffine, RandomPerspective, ColorJitter, GaussianBlur, RandomEr...

Readme6 agohuggingface.co/hermitkk/alphabet-classifier

--- license: mit tags: - image-classification - onnx - pytorch - letters - digits - alphanumeric - emnist datasets: - emnist metrics: - accuracy --- # Alpha-Numeric Classifier (0–9, A–Z, blank) A lightweight CNN that classifies grayscale images of handwritten digits (0–9), uppercase letters (A–Z), and blank boxes, trained on a combined EMNIST Digits + Letters dataset with synthetic blank images. ## Model Details | Property | Value | |---|---| | Architecture | SmallAlphaNet (depthwise-separable CNN) | | Parameters | 38,373 | | Input | 64 × 64 grayscale image | | Output | 37-class softmax (0–9, A–Z, blank) | | Format | ONNX (opset 17) | | Val accuracy | **90.18%** | The architecture uses depthwise-separable convolutions (like MobileNet) to stay small and fast while achieving strong accuracy. ## Training Configuration | Setting | Value | |---|---| | Dataset | EMNIST Digits + Letters (`train` split) + synthetic blanks | | Train samples | 374,800 (240,000 digits + 124,800 letters + 10,000 blank) | | Val samples | 62,800 (40,000 digits + 20,800 letters + 2,000 blank) | | Optimizer | AdamW (lr=0.001, weight_decay=1e-4) | | Scheduler | CosineAnnealingLR | | Batch size | 256 | | Max epochs | 30 | | Early stopping | patience=8 | | Augmentation | RandomAffine, RandomPerspective, ColorJitter, GaussianBlur, RandomErasing | ## Classes | Index | Character | |---|---| | 0–9 | Digits `0`–`9` | | 10–35 | Uppercase letters `A`–`Z` | | 36 | Blank (empty box) | ## Per-class Performance ### Digits | Class | Precision | Recall | F1 | |---|---|---|---| | 0 | 0.834 | 0.979 | 0.900 | | 1 | 0.569 | 0.970 | **0.717** | | 2 | 0.950 | 0.975 | 0.962 | | 3 | 0.993 | 0.995 | 0.994 | | 4 | 0.966 | 0.980 | 0.973 | | 5 | 0.967 | 0.956 | 0.961 | | 6 | 0.977 | 0.985 | 0.981 | | 7 | 0.989 | 0.995 | 0.992 | | 8 | 0.977 | 0.987 | 0.982 | | 9 | 0.909 | 0.983 | 0.945 | ### Letters | Class | Precision | Recall | F1 | |---|---|---|---| | A | 0.913 | 0.933 | 0.923 | | B | 0.941 | 0.905 | 0.923 | | C | 0.950 | 0.976 | 0.963 | | D | 0.953 | 0.915 | 0.934 | | E | 0.973 | 0.963 | 0.968 | | F | 0.976 | 0.953 | 0.964 | | G | 0.861 | 0.658 | **0.746** | | H | 0.948 | 0.939 | 0.943 | | I | 0.676 | 0.471 | **0.555** | | J | 0.947 | 0.924 | 0.935 | | K | 0.985 | 0.971 | 0.978 | | L | 0.876 | 0.256 | **0.397** | | M | 0.974 | 0.995 | 0.985 | | N | 0.953 | 0.956 | 0.954 | | O | 0.696 | 0.1...

Note6 agohuggingface.co/hermitkk/alphabet-classifier

Source context: 0 downloads · 0 likes · Pipeline image-classification · Repo hermitkk/alphabet-classifier