Update README.md

README.md

@@ -48,102 +48,6 @@ It uses a CTC head so it can handle variable-length text without needing segment
 ```bash
 pip install torch torchvision pillow
 ```
-
-### Option A — using `model.pt` (state_dict)
-
-```python
-import torch
-import torch.nn as nn
-import numpy as np
-from PIL import Image
-from huggingface_hub import hf_hub_download
-
-# ── 1. Model definition ──────────────────────────────────────────────────────
-class KhmerOCR_DTWG(nn.Module):
-    def __init__(self, num_chars, hidden_size=256):
-        super().__init__()
-        self.cnn = nn.Sequential(
-            self._conv(1, 32),  nn.MaxPool2d(2, 2),
-            self._conv(32, 64), nn.MaxPool2d(2, 2),
-            self._conv(64, 128),
-            self._conv(128, 128),
-            nn.MaxPool2d((2, 1), (2, 1)),
-            self._conv(128, 256),
-            self._conv(256, 256),
-            nn.MaxPool2d((4, 1), (4, 1)),
-        )
-        self.lstm1 = nn.LSTM(256, hidden_size, bidirectional=True, batch_first=True)
-        self.fc1   = nn.Linear(hidden_size * 2, hidden_size)
-        self.lstm2 = nn.LSTM(hidden_size, hidden_size, bidirectional=True, batch_first=True)
-        self.fc    = nn.Linear(hidden_size * 2, num_chars + 1)
-
-    def _conv(self, i, o):
-        return nn.Sequential(
-            nn.Conv2d(i, o, 3, 1, 1, bias=False),
-            nn.BatchNorm2d(o),
-            nn.ReLU(inplace=True),
-        )
-
-    def forward(self, x):
-        x = self.cnn(x)
-        x = x.squeeze(2).permute(0, 2, 1)
-        x, _ = self.lstm1(x)
-        x = torch.relu(self.fc1(x))
-        x, _ = self.lstm2(x)
-        x = self.fc(x)
-        return x.permute(1, 0, 2)
-
-# ── 2. Vocabulary ─────────────────────────────────────────────────────────────
-TOKENS = (
-    "abcdefghijklmnopqrstuvwxyz"
-    "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
-    "0123456789"
-    "កខគឃងចឆជឈញដឋឌឍណតថទធនបផពភមយរលវឝឞសហឡអឣឤឥឦឧឩឪឫឬឭឮឯឰឱឲឳ"
-    "ាិីឹឺុូួើឿៀេែៃោៅំះៈ៉៊់៌៍៎៏័៑្។៕៖ៗ៘៛៝"
-    "០១២៣៤៥៦៧៨៩៳"
-    "!@#$%^&*()-_=+[]{};:'\",.<>?/|\\ "
-)
-NUM_CHARS = len(TOKENS)
-idx2char  = {i + 1: c for i, c in enumerate(TOKENS)}
-
-# ── 3. Load model ─────────────────────────────────────────────────────────────
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-weights_path = hf_hub_download(repo_id="phonsobon/mini-ocr", filename="model.pt")
-model = KhmerOCR_DTWG(NUM_CHARS).to(device)
-model.load_state_dict(torch.load(weights_path, map_location=device))
-model.eval()
-
-# ── 4. Helpers ────────────────────────────────────────────────────────────────
-def load_image(path):
-    img = Image.open(path).convert("L")
-    w, h = img.size
-    new_w = int(w / h * 32)
-    img = img.resize((new_w, 32))
-    img = np.array(img, dtype=np.float32) / 255.0
-    return torch.tensor(img).unsqueeze(0).unsqueeze(0)  # (1, 1, 32, W)
-
-def ctc_decode(logits):
-    preds = torch.argmax(logits, dim=2)[:, 0].cpu().numpy()
-    prev, text = -1, []
-    for p in preds:
-        if p != prev and p != 0:
-            text.append(idx2char.get(p, ""))
-        prev = p
-    return "".join(text)
-
-# ── 5. Inference ──────────────────────────────────────────────────────────────
-img = load_image("your_image.png").to(device)
-
-with torch.no_grad():
-    logits = model(img)
-    result = ctc_decode(logits)
-
-print("OCR result:", result)
-```
-
-### Option B — TorchScript (no class needed)
-
 ```python
 import torch
 import numpy as np