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.
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.
