Japanese Character Recognition CNN
A Convolutional Neural Network (CNN) for classifying individual Japanese characters from 64x64 grayscale images. Three model versions are available.
v3 — PyTorch/ONNX (recommended)
KanjiDNN_v3.onnx — a compact 4-block CNN optimized for finger-drawn input on touchscreens.
| Property | Value |
|---|---|
| Framework | PyTorch, exported to ONNX |
| Classes | 3,181 (2,140 Joyo kanji + 859 JIS Level 1 kanji + 181 kana + 1 REJECT) |
| Input | 64x64 grayscale, binarized, ink=1.0 / background=0.0 |
| Parameters | ~2.4M |
| File size | 11 MB |
| Training data | KanjiVG SVG strokes with finger-drawing augmentation + ETL9G handwriting samples |
Architecture
4x ConvBlock(Conv3x3 → BN → ReLU → Conv3x3 → BN → ReLU → MaxPool2x2 → Dropout)
channels: 32 → 64 → 128 → 256
AdaptiveAvgPool → Linear(256,512) → BN → ReLU → Dropout(0.4) → Linear(512, num_classes)
Preprocessing
Images must be preprocessed identically to training (see KanjiDNN_v3_preprocessing_config.json):
- Binarize at threshold 200
- Crop to ink bounding box
- Add margin (10% of the larger dimension)
- Resize to 64x64 (preserve aspect ratio, center on canvas)
- Normalize:
(255 - pixel) / 255(ink=1.0, background=0.0)
Rejection
Dual-layer rejection (see KanjiDNN_v3_inference_config.json):
- Explicit
REJECTclass (index 3180) trained on romaji, digits, punctuation, scribbles, and blanks - Confidence threshold: force reject when top softmax probability < 0.5
Usage (ONNX Runtime)
import onnxruntime as ort
import numpy as np
import json
session = ort.InferenceSession("KanjiDNN_v3.onnx")
classes = json.load(open("KanjiDNN_v3_classes.json"))
# image: preprocessed 64x64 float32 array, shape (1, 1, 64, 64)
image = np.expand_dims(np.expand_dims(preprocessed, 0), 0).astype(np.float32)
logits = session.run(None, {"input": image})[0]
probs = np.exp(logits) / np.exp(logits).sum()
top_idx = np.argmax(probs)
print(f"Prediction: {classes[top_idx]} ({probs[0][top_idx]:.1%})")
Why v3?
Built to replace v1/v2, whose precision suffers on real finger input — their inference pipeline skips bounding-box centering and normalization. v3 uses KanjiVG strokes augmented to approximate finger-drawing (thick, imprecise strokes, no pressure taper) plus ETL9G handwriting samples, with a rigorous preprocessing pipeline that must be replicated at inference time.
Source code
Training pipeline: github.com/gaiseras/kanjirec (if public) — includes dataset generation, augmentation, training, evaluation, and ONNX export scripts.
v1 & v2 — Keras
KanjiDNN_v1.keras and KanjiDNN_v2.keras — the original Keras models.
Character classes
- Kanji, Hiragana & Katakana (kana), Romaji (Latin letters), Digits (0-9), Common punctuation marks
Training data
- Self-generated character images
- SVG graphics from KanjiVG
- ETL9G dataset from the ETL Character Database
Usage (Keras)
import os
os.environ["KERAS_BACKEND"] = "jax" # or "torch", "tensorflow"
import keras
model = keras.saving.load_model("hf://gaiseras/kanjiDNN")