20：GLM-OCR 深度解析：轻量级多模态OCR的技术突破

# CogViT视觉编码器核心实现
class CogViT(nn.Module):
    def __init__(self, config):
        super().__init__()
        # 基础ViT架构
        self.vit = VisionTransformer(
            img_size=config.img_size,
            patch_size=config.patch_size,
            embed_dim=config.embed_dim,
            depth=config.depth,
            num_heads=config.num_heads,
            mlp_ratio=config.mlp_ratio
        )
        
        # 认知增强模块
        self.cognitive_module = nn.Sequential(
            nn.Linear(config.embed_dim, config.embed_dim),
            nn.GELU(),
            nn.Linear(config.embed_dim, config.embed_dim)
        )
        
        # 多尺度特征融合
        self.feature_fusion = nn.ModuleList([
            nn.Conv2d(config.embed_dim // (2**i), config.embed_dim, kernel_size=1)
            for i in range(config.num_scales)
        ])
    
    def forward(self, x):
        # 提取基础特征
        features = self.vit(x)
        
        # 认知增强
        enhanced_features = self.cognitive_module(features)
        
        # 多尺度特征融合
        fused_features = []
        for i, fusion in enumerate(self.feature_fusion):
            scale_feature = F.interpolate(
                features[:, :, ::2**i, ::2**i],
                size=features.shape[2:],
                mode='bilinear'
            )
            fused_features.append(fusion(scale_feature))
        
        # 特征融合
        final_features = enhanced_features
        for feat in fused_features:
            final_features += feat
        
        return final_features

# 多任务识别头实现
class MultiTaskOCRHead(nn.Module):
    def __init__(self, config):
        super().__init__()
        # 文本检测头
        self.text_detector = nn.Sequential(
            nn.Conv2d(config.embed_dim, 256, kernel_size=3, padding=1),
            nn.GELU(),
            nn.Conv2d(256, 2, kernel_size=1)  # 文本/背景二分类
        )
        
        # 文本识别头
        self.text_recognizer = nn.Sequential(
            nn.Linear(config.embed_dim, 512),
            nn.GELU(),
            nn.Linear(512, config.vocab_size)
        )
        
        # 表格分析头
        self.table_analyzer = nn.Sequential(
            nn.Conv2d(config.embed_dim, 256, kernel_size=3, padding=1),
            nn.GELU(),
            nn.Conv2d(256, 4, kernel_size=1)  # 表格线检测
        )
        
        # 印章检测头
        self.stamp_detector = nn.Sequential(
            nn.Conv2d(config.embed_dim, 256, kernel_size=3, padding=1),
            nn.GELU(),
            nn.Conv2d(256, 2, kernel_size=1)  # 印章/非印章二分类
        )
    
    def forward(self, features):
        # 文本检测
        text_detection = self.text_detector(features)
        
        # 文本识别
        text_features = F.adaptive_avg_pool2d(features, (1, 1)).squeeze()
        text_recognition = self.text_recognizer(text_features)
        
        # 表格分析
        table_analysis = self.table_analyzer(features)
        
        # 印章检测
        stamp_detection = self.stamp_detector(features)
        
        return {
            'text_detection': text_detection,
            'text_recognition': text_recognition,
            'table_analysis': table_analysis,
            'stamp_detection': stamp_detection
        }

# 强化学习训练实现
class OCRRLHF:
    def __init__(self, model, tokenizer):
        self.model = model
        self.tokenizer = tokenizer
        self.reward_model = RewardModel()
        self.ppo_trainer = PPOTrainer(model, reward_model)
    
    def train(self, dataset, epochs=10):
        """训练模型"""
        for epoch in range(epochs):
            for batch in dataset:
                # 1. 模型生成
                outputs = self.model.generate(batch['images'])
                
                # 2. 计算奖励
                rewards = self.reward_model.compute_reward(
                    batch['images'],
                    outputs,
                    batch['ground_truth']
                )
                
                # 3. PPO更新
                loss = self.ppo_trainer.step(
                    batch['images'],
                    outputs,
                    rewards
                )
                
                print(f"Epoch {epoch}, Loss: {loss.item()}")
    
    def evaluate(self, test_dataset):
        """评估模型性能"""
        correct = 0
        total = 0
        
        for batch in test_dataset:
            outputs = self.model.generate(batch['images'])
            correct += self.calculate_accuracy(outputs, batch['ground_truth'])
            total += len(batch['ground_truth'])
        
        accuracy = correct / total
        print(f"Evaluation Accuracy: {accuracy:.4f}")
        return accuracy

# 克隆仓库
git clone https://github.com/ZhipuAI/GLM-OCR.git

# 安装依赖
pip install -r requirements.txt

# 下载模型
python download_model.py

# 启动服务
python server.py --port 8000

from glm_ocr import GLMOCR

# 初始化模型
ocr = GLMOCR(model_path='path/to/model')

# 识别单张图像
result = ocr.recognize('test.jpg')
print(result)

# 识别批量图像
results = ocr.recognize_batch(['test1.jpg', 'test2.jpg'])
for i, res in enumerate(results):
    print(f"Image {i+1}: {res}")

# 表格解析
table_result = ocr.recognize_table('table.jpg')
print(table_result)

# 印章识别
stamp_result = ocr.recognize_stamp('document.jpg')
print(stamp_result)

# 识别单张图像
glm-ocr recognize --image test.jpg --output result.json

# 批量识别
glm-ocr batch --images_dir ./images --output_dir ./results

# 表格解析
glm-ocr table --image table.jpg --output table.csv

# 启动API服务
glm-ocr serve --port 8000