MVP model. Production upgrade: swap to ppocrv5 variant (same interface, better accuracy). See config.json → architecture_variant for programmatic detection.

CTC-based text recognition model for Thai National ID card thai fields, designed for on-device inference at 30fps on mobile.

Metric	Value
Architecture	SimpleCRNN (MVP)
Variant	`crnn`
ExactMatch	95.3%
CharAccuracy	99.1%
Parameters	3,059,535
Vocab size	79
Best epoch	116

Quick Start

python

from huggingface_hub import hf_hub_download

model_path = hf_hub_download("chayuto/thai-id-ocr-crnn-thai-reader", "model.pt")
vocab_path = hf_hub_download("chayuto/thai-id-ocr-crnn-thai-reader", "vocab.txt")
config = hf_hub_download("chayuto/thai-id-ocr-crnn-thai-reader", "config.json")

Architecture

SimpleCRNN — CNN (4-layer) + BiLSTM (2-layer) + CTC decoder.

Input: [B, 3, 48, 320]  (RGB, normalized to [-1, 1])
  → CNN: 32→64→128→256 channels, BatchNorm+ReLU, MaxPool(2,2)×3
  → AdaptiveAvgPool2d((1, None))  → T=40 time steps
  → BiLSTM: hidden=256, layers=2, dropout=0.1
  → Linear(512 → 79)
  → CTC decode (blank=0, collapse repeats)
Output: Unicode string

Field Details

Zone: text_thai_zone (names, addresses, dates, religion)
Charset: 42 consonants + 16 vowels + 7 marks + Arabic digits + punctuation (78 chars + CTC blank)
FP16 recommended for tone mark preservation at quantization
v2: Fixed 78-char vocab, 95.3% ExactMatch, 99.1% CharAcc

Input Preprocessing

python

import cv2
import numpy as np

def preprocess(img_path, height=48, max_width=320):
    img = cv2.imread(img_path)
    h, w = img.shape[:2]
    ratio = height / h
    new_w = min(int(w * ratio), max_width)
    img = cv2.resize(img, (new_w, height))
    # Pad to max_width with white
    if new_w < max_width:
        pad = np.full((height, max_width - new_w, 3), 255, dtype=np.uint8)
        img = np.concatenate([img, pad], axis=1)
    # Normalize to [-1, 1]
    img = img.astype(np.float32) / 255.0
    img = (img - 0.5) / 0.5
    return np.transpose(img, (2, 0, 1))  # CHW

CTC Decoding

python

def ctc_decode(indices, vocab_chars, blank_idx=0):
    chars, prev = [], -1
    for idx in indices:
        if idx != blank_idx and idx != prev:
            if 1 <= idx <= len(vocab_chars):
                chars.append(vocab_chars[idx - 1])
        prev = idx
    return "".join(chars)

Loading the Model

python

import torch
import torch.nn as nn

class SimpleCRNN(nn.Module):
    def __init__(self, num_classes, img_h=48):
        super().__init__()
        self.cnn = nn.Sequential(
            nn.Conv2d(3, 32, 3, padding=1), nn.BatchNorm2d(32), nn.ReLU(), nn.MaxPool2d(2, 2),
            nn.Conv2d(32, 64, 3, padding=1), nn.BatchNorm2d(64), nn.ReLU(), nn.MaxPool2d(2, 2),
            nn.Conv2d(64, 128, 3, padding=1), nn.BatchNorm2d(128), nn.ReLU(), nn.MaxPool2d(2, 2),
            nn.Conv2d(128, 256, 3, padding=1), nn.BatchNorm2d(256), nn.ReLU(),
            nn.AdaptiveAvgPool2d((1, None)),
        )
        self.rnn = nn.LSTM(256, 256, num_layers=2, bidirectional=True, batch_first=True, dropout=0.1)
        self.fc = nn.Linear(512, num_classes)

    def forward(self, x):
        features = self.cnn(x).squeeze(2).permute(0, 2, 1)
        rnn_out, _ = self.rnn(features)
        return self.fc(rnn_out).permute(1, 0, 2)  # (T, B, C) for CTC

model = SimpleCRNN(num_classes=79)
model.load_state_dict(torch.load(model_path, map_location="cpu"))
model.eval()

Pipeline Context

This model is one of 3 Reader experts in the Thai ID Nano OCR pipeline:

Camera Frame → YOLO26n Finder (5-class, single pass)
  → num_id_zone, num_dob_zone    → Numeric Reader
  → text_eng_zone                → English Reader
  → text_thai_zone               → Thai Reader
  → Validator (Mod11 checksum, date logic)

Total pipeline: <15 MB, 30fps on mobile.

Files

File	Description
`model.pt`	PyTorch `state_dict` (~12 MB)
`vocab.txt`	Character vocabulary, one per line (`<space>` = space). CTC blank is implicit at index 0.
`config.json`	Architecture params, training metadata, charset

License

MIT