Namsel BUDA CNN — Tibetan Character Classifier

Lightweight CNN for Tibetan character recognition, replacing the hand-crafted feature pipeline of the Namsel OCR system with learned CNN features used in TradutorBUDA.

94.47% validation accuracy on 1,020 Tibetan character classes. Trained on 47,777 samples derived from the Namsel OCR project dataset. Exported to ONNX (FP32 and INT8) for CPU deployment.

Model Files

Architecture — TibetanCNN

CPU-optimized CNN with depthwise separable convolutions and residual connections.

Input (1 × 32 × 32 grayscale)
→ Stem:  Conv2d(1→32, 3×3) + BN + ReLU          [32 × 32 × 32]
→ Down1: DepthwiseSeparableConv(32→64, stride=2)  [64 × 16 × 16]
→ Res1:  ResidualBlock(64)                        [64 × 16 × 16]
→ Down2: DepthwiseSeparableConv(64→128, stride=2) [128 × 8 × 8]
→ Res2:  ResidualBlock(128)                       [128 × 8 × 8]
→ Down3: DepthwiseSeparableConv(128→256,stride=2) [256 × 4 × 4]
→ GlobalAveragePool → Dropout(0.3) → FC(256→1020)

• Parameters: 353,596 (~8–18× fewer than standard convolutions)
• Inference: < 5 ms per character on CPU

Results

The CNN+Transformer hybrid achieves +1.05% accuracy but uses 39% more parameters and takes 80% longer to train. For CPU OCR deployment, the lightweight CNN with ONNX/INT8 is the practical choice.

Dataset

• Source: Namsel OCR project training data
• Total samples: 47,777 (after deduplication)
• Classes: 1,020 Tibetan character classes
• Image format: 32×32 binary (black/white) glyphs, 1 channel
• Split: 85% train (40,611) / 15% validation (7,166), stratified

Data sources within the dataset:

• font-draw-samples: 16,320 synthetically rendered samples
• pkl files: 19,613 additional character data
• ui_samples: 9,229 UI-extracted samples
• normalized_3216_to_3232: 7,117 resized/normalized characters
• tibcharsamples: 1,619 manually collected images
• symbols: 15 punctuation/special symbols

Training Configuration

• Optimizer: AdamW (lr=1e-3, weight_decay=1e-4)
• LR Schedule: 5-epoch linear warmup → cosine annealing to 1e-6
• Loss: CrossEntropyLoss with class weighting + label smoothing (0.1)
• Augmentation: Mixup (alpha=0.2)
• Mixed precision: AMP/BF16 on NVIDIA A100 (40 GB)
• Batch size: 512
• Epochs: 100 (early stopping patience=15, did not trigger)
• Best epoch: 90

Usage

PyTorch

import torch
import numpy as np
from model import TibetanCNN
import json

with open("label_mapping.json") as f:
    mapping = json.load(f)

checkpoint = torch.load("best_model.pth", map_location="cpu", weights_only=False)
model = TibetanCNN(num_classes=mapping["num_classes"], dropout=0.3)
model.load_state_dict(checkpoint["model_state_dict"])
model.eval()

# image: numpy array of shape (32, 32), binary pixels (0 or 1)
img_tensor = torch.tensor(image, dtype=torch.float32).unsqueeze(0).unsqueeze(0)  # [1,1,32,32]
with torch.no_grad():
    logits = model(img_tensor)
pred_idx = logits.argmax(dim=1).item()
pred_label = mapping["idx_to_label"][str(pred_idx)]

ONNX (CPU, fastest with INT8)

import onnxruntime as ort
import numpy as np

session = ort.InferenceSession("best_model_int8.onnx", providers=["CPUExecutionProvider"])

# image: numpy array (32, 32), float32, values 0.0 or 1.0
img = image.astype(np.float32).reshape(1, 1, 32, 32)
logits = session.run(None, {"image": img})[0]
pred_idx = logits.argmax(axis=1)[0]

Using predict.py

from predict import TibetanCNNPredictor

predictor = TibetanCNNPredictor("best_model.pth", "label_mapping.json")
top3 = predictor.predict_top_k(image_array, k=3)
# returns list of (label_idx, confidence) tuples

Background

Tibetan script OCR is an underserved area in document digitization. The original Namsel OCR system (Rowinski, 2016) used hand-crafted features (Zernike moments, Sobel gradients, pixel transition counts) with a scikit-learn classifier, struggling with font variation and low-quality images.

This model replaces that pipeline with learned CNN features, achieving significantly higher accuracy and better generalization across font styles.

References

• Rowinski, T. "Namsel OCR" (2016). https://escholarship.org/uc/item/6d5781k5
• Wang et al. "Unsupervised Tibetan Historical Document Recognition" (2024). https://www.mdpi.com/2076-3417/14/5/2142
• Loshchilov & Hutter. "Decoupled Weight Decay Regularization" (ICLR 2019). https://arxiv.org/abs/1711.05101
• Zhang et al. "mixup: Beyond Empirical Risk Minimization" (ICLR 2018). https://arxiv.org/abs/1710.09412