Update README.md

README.md

@@ -24,26 +24,114 @@ trained with CTC loss to extract text from images.
 ## Installation
 
 ```bash
-pip install torch torchvision huggingface_hub
+!pip install torch torchvision huggingface_hub
 
 
 
 ## Usage Example
 
+import json
 import torch
+import torch.nn as nn
 from PIL import Image
 import torchvision.transforms.functional as TF
 import cv2
 import matplotlib.pyplot as plt
 from huggingface_hub import hf_hub_download
 
-# --- Load model from HF ---
+# -----------------------------
+# 1️⃣ Device
+# -----------------------------
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# -----------------------------
+# 2️⃣ Load vocab
+# -----------------------------
+vocab_path = hf_hub_download(repo_id="farbodpya/Persian-OCR", filename="vocab.json")
+with open(vocab_path, "r", encoding="utf-8") as f:
+    vocab = json.load(f)
+char_to_idx = vocab["char_to_idx"]
+idx_to_char = {int(k): v for k, v in vocab["idx_to_char"].items()}
+
+# -----------------------------
+# 3️⃣ Model definition
+# -----------------------------
+def GN(c, groups=16): return nn.GroupNorm(min(groups, c), c)
+
+class LightResNetCNN(nn.Module):
+    def __init__(self, in_channels=1, adaptive_height=8):
+        super().__init__()
+        self.adaptive_height = adaptive_height
+        self.layer1 = nn.Sequential(nn.Conv2d(in_channels, 32, 3, 1, 1), GN(32), nn.ReLU(), nn.MaxPool2d(2, 2))
+        self.layer2 = nn.Sequential(nn.Conv2d(32, 64, 3, 1, 1), GN(64), nn.ReLU(), nn.MaxPool2d(2, 2))
+        self.layer3 = nn.Sequential(nn.Conv2d(64, 128, 3, 1, 1), GN(128), nn.ReLU(), nn.MaxPool2d(2, 2))
+        self.layer4 = nn.Sequential(nn.Conv2d(128, 256, 3, 1, 1), GN(256), nn.ReLU())
+        self.layer5 = nn.Sequential(nn.Conv2d(256, 256, 3, 1, 1), GN(256), nn.ReLU())
+        self.layer6 = nn.Sequential(nn.Conv2d(256, 128, 3, 1, 1), GN(128), nn.ReLU())
+        self.adaptive_pool = nn.AdaptiveAvgPool2d((self.adaptive_height, None))
+    def forward(self, x):
+        for i in range(1, 7):
+            x = getattr(self, f"layer{i}")(x)
+        x = self.adaptive_pool(x)
+        return x
+
+class PositionalEncoding(nn.Module):
+    def __init__(self, d_model, max_len=2000):
+        super().__init__()
+        pe = torch.zeros(max_len, d_model)
+        position = torch.arange(0, max_len).unsqueeze(1)
+        div_term = torch.exp(torch.arange(0, d_model, 2) * (-torch.log(torch.tensor(10000.0)) / d_model))
+        pe[:, 0::2] = torch.sin(position * div_term)
+        pe[:, 1::2] = torch.cos(position * div_term)
+        self.register_buffer("pe", pe.unsqueeze(0))
+    def forward(self, x):
+        return x + self.pe[:, :x.size(1), :]
+
+class CNN_Transformer_OCR(nn.Module):
+    def __init__(self, num_classes, d_model=1280, nhead=16, num_layers=8, dropout=0.2):
+        super().__init__()
+        self.cnn = LightResNetCNN(in_channels=1, adaptive_height=8)
+        self.proj = nn.Linear(128 * 8, d_model)
+        self.posenc = PositionalEncoding(d_model)
+        encoder_layer = nn.TransformerEncoderLayer(d_model, nhead, batch_first=True, dropout=dropout)
+        self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)
+        self.fc = nn.Linear(d_model, num_classes)
+    def forward(self, x):
+        f = self.cnn(x)
+        B, C, H, W = f.size()
+        f = f.permute(0, 3, 1, 2).reshape(B, W, C * H)
+        f = self.posenc(self.proj(f))
+        out = self.transformer(f)
+        out = self.fc(out)
+        return out.log_softmax(2)
+
+# -----------------------------
+# 4️⃣ Load model weights
+# -----------------------------
 model_path = hf_hub_download(repo_id="farbodpya/Persian-OCR", filename="pytorch_model.bin")
 model = CNN_Transformer_OCR(num_classes=len(idx_to_char)+1).to(device)
 model.load_state_dict(torch.load(model_path, map_location=device))
 model.eval()
 
-# --- Define transforms ---
+# -----------------------------
+# 5️⃣ Greedy decoder
+# -----------------------------
+def greedy_decode(output, idx_to_char):
+    output = output.argmax(2)
+    texts = []
+    for seq in output:
+        prev = -1
+        chars = []
+        for idx in seq.cpu().numpy():
+            if idx != prev and idx != 0:
+                chars.append(idx_to_char.get(idx, ""))
+            prev = idx
+        texts.append("".join(chars))
+    return texts
+
+# -----------------------------
+# 6️⃣ Transforms
+# -----------------------------
 class OCRTestTransform:
     def __init__(self, img_height=64, max_width=1600):
         self.img_height = img_height
@@ -61,7 +149,9 @@ class OCRTestTransform:
 
 transform_test = OCRTestTransform()
 
-# --- Line segmentation ---
+# -----------------------------
+# 7️⃣ Line segmentation
+# -----------------------------
 def segment_lines_precise(image_path, min_line_height=12, margin=6, visualize=False):
     img = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
     _, binary = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
@@ -86,7 +176,9 @@ def segment_lines_precise(image_path, min_line_height=12, margin=6, visualize=Fa
             plt.show()
     return lines
 
-# --- OCR function ---
+# -----------------------------
+# 8️⃣ OCR function
+# -----------------------------
 def ocr_page(image_path, visualize=False):
     lines = segment_lines_precise(image_path, visualize=visualize)
     all_texts = []
@@ -99,7 +191,10 @@ def ocr_page(image_path, visualize=False):
         print(f"Line {idx}: {pred_text}")
     return "\n".join(all_texts)
 
-# --- Example ---
-img_path = "example.png"  
+# -----------------------------
+# 9️⃣ Example usage
+# -----------------------------
+img_path = "example.png"  # put your own image path here
 final_text = ocr_page(img_path, visualize=True)
 print("\n=== Final OCR Page ===\n", final_text)
+