AsterNet: Vietnamese Neural Optical Character Recognition (v1.0 Production)

AsterNet is an ultra-fast, non-autoregressive neural Optical Character Recognition engine tailored for Vietnamese typography, administrative records (CCCD, passports, licenses), financial invoices, and mobile scene captures.

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1. Architectural Blueprint & Parameter Breakdown

AsterNet abandons slow token-by-token autoregressive decoding in favor of an optimized end-to-end non-autoregressive sequence architecture.

graph TD
    A["Input Image Strip (32 x W x 3)"] --> B["HP-Stem: High-Pass Spatial Feature Filter"]
    B --> C["Backbone: Inverted Residual SE-ResNet (2.53M Params)"]
    C --> D["Map-to-Sequence (Feature Map -> 1D Temporal Slices)"]
    D --> E["Bidirectional LSTM (2 Layers, 256 Dim - 3.15M Params)"]
    E --> F["Multi-Head Self-Attention Fusion (4 Heads - 4.21M Params)"]
    F --> G["CTC Prediction Head (10.65M Total Params)"]
    G --> H["Neuro-Symbolic Linguistic Rescorer (Phonology & N-gram)"]
    H --> I["Output Vietnamese Text (Unicode NFC Standard)"]

Complete Parameter Profile (10,649,106 Total Parameters)

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2. Mathematical Formulations & Novel Algorithms

2.1. High-Pass Spatial Feature Stem (HP-Stem)

Standard convolutional downsampling kernels act as low-pass filters, frequently obliterating subtle Vietnamese diacritics. HP-Stem introduces a learnable residual high-pass kernel $K_{hp}$:

$$F_{stem}(X) = \text{Conv}{3\times3}(X) + \gamma \cdot (X * K{hp})$$

where $\gamma$ is a dynamic scalar parameter that amplifies edge gradients of high-frequency diacritic strokes before entering the pooling pyramid.

2.2. Inverted Residual with Squeeze-and-Excitation (SE-ResNet)

To suppress background paper discoloration, scanner artifacts, and watermark textures, AsterNet incorporates SE channel gating:

$$z_c = \frac{1}{H \times W} \sum_{i=1}^H \sum_{j=1}^W u_c(i, j)$$

$$s = \sigma\left(W_2 \cdot \text{ReLU}(W_1 z)\right)$$

$$\tilde{X}_c = s_c \cdot u_c$$

This mechanism dynamically recalibrates channel-wise feature responses, effectively discarding colored watermark overlays and background texture.

2.3. Contrast-Based Auto-Polarity Engine

Traditional CTC decoders fail catastrophically on inverted text strips (white text on dark backgrounds, camera timestamps, dark mode), outputting repetitive noise. AsterNet implements a mathematical contrast polarity check before tensor normalization:

$$\Delta_{bright} = P_{90}(X_{center}) - \text{Median}(X_{border})$$

$$\Delta_{dark} = \text{Median}(X_{border}) - P_{10}(X_{center})$$

$$\text{If } (\Delta_{bright} > \Delta_{dark} \text{ and } \Delta_{bright} > 20) \implies X \leftarrow (255 - X)$$

This guarantees invariant forward activations regardless of background luminance.

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3. Real-World CPU Performance & Hardware Matrix

AsterNet is engineered for ultra-lightweight execution without requiring expensive GPU servers. Tested on 100 representative lines of the National Vietnamese Benchmark Suite (vi_00 / InkData):

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4. Benchmark Accuracy Comparison vs VietOCR

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5. Multi-Language Deployment & Integration Guide

5.1. Python (Native PyTorch)

import torch
from PIL import Image

# 1. Download model weights
# wget https://huggingface.co/asterxlabs/asternet-vietnamese-ocr/resolve/main/asternet_best.pth

# 2. Run inference using predictor
from asterocr.inference.predictor import AsterPredictor

predictor = AsterPredictor(model_path="asternet_best.pth")
text = predictor.predict("sample_line.jpg")
print("Recognized Text:", text)

5.2. Node.js / TypeScript (ONNX Runtime)

import * as ort from "onnxruntime-node";
import * as fs from "fs";

async function runOCR(imageBuffer: Buffer): Promise<string> {
    const session = await ort.InferenceSession.create("asternet.onnx");
    // Preprocess tensor (1, 3, 32, W)
    // Run session.run({ input: tensor });
    return "Output string";
}

5.3. C++ (Production High-Concurrency Engine)

#include <onnxruntime_cxx_api.h>
#include <opencv2/opencv.hpp>

int main() {
    Ort::Env env(ORT_LOGGING_LEVEL_WARNING, "AsterNetOCR");
    Ort::Session session(env, L"asternet.onnx", Ort::SessionOptions{});
    // Load OpenCV cv::Mat -> 32px height -> Run Session
    return 0;
}

5.4. C# / .NET (Enterprise Desktop & WPF)

using Microsoft.ML.OnnxRuntime;
using Microsoft.ML.OnnxRuntime.Tensors;

var session = new InferenceSession("asternet.onnx");
// Run inference directly in desktop or server .NET applications

5.5. Go (Backend Microservice)

package main
import (
    "fmt"
    ort "github.com/yalue/onnxruntime_go"
)

func main() {
    // Load AsterNet ONNX model into Go microservice
    fmt.Println("AsterNet OCR Microservice ready on port 8080")
}

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6. Model Artifacts & File Inventory

• asternet.onnx (40.76 MB): Production ONNX runtime model for Python, Node.js, Flutter, C++, C#, Go, and Web.
• asternet_best.pth (40.78 MB): PyTorch weights trained on 1.56M unified Vietnamese document samples.
• config.json: Hardware, architectural, and component configuration parameters.
• vocab.json: 227-character vocabulary dictionary mapping tokens to characters.
• README.md: Official technical specification and benchmark documentation.

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7. License & Citation

AsterNet v1.0 is published under the MIT License. Free for commercial and non-commercial utilization.

Developed by asterxlabs (2026).