# Conceptual multimodal document understanding architecture

class MultimodalDocumentModel:
    """
    Vision-language model for document understanding.
    Based on architecture similar to GPT-4V, Gemini.
    """

    def __init__(self):
        # Vision encoder: Process document images
        self.vision_encoder = VisionTransformer(  # See: /articles/vision-transformers-ocr
            patch_size=16,
            embed_dim=1024,
            depth=24,
            num_heads=16
        )

        # Language model: Process and generate text
        self.language_model = TransformerDecoder(
            vocab_size=50000,
            embed_dim=1024,
            depth=24,
            num_heads=16
        )

        # [Cross-modal attention](/articles/attention-mechanisms-ocr): Link vision and language
        self.cross_attention = CrossModalAttention(
            dim=1024,
            num_heads=16
        )

    def understand_document(self, image, query):
        """
        Process document image with contextual query.

        Args:
            image: Document image
            query: User query or task description

        Returns:
            Structured understanding of document
        """
        # Encode image into visual features
        visual_features = self.vision_encoder(image)

        # Encode query
        query_embedding = self.language_model.encode(query)

        # Cross-modal attention: Link query to relevant image regions
        attended_features = self.cross_attention(
            query_embedding,
            visual_features
        )

        # Generate response
        response = self.language_model.generate(
            attended_features,
            max_length=500
        )

        return {
            'text': response,
            'attention_map': self.cross_attention.get_attention_map(),
            'confidence': self.calculate_confidence()
        }

# Layout-aware document transformer

class LayoutAwareTransformer:
    """
    Transformer that incorporates spatial layout information.
    Inspired by LayoutLMv3 architecture.
    """

    def __init__(self):
        self.text_embedding = nn.Embedding(vocab_size, 768)
        self.position_embedding = nn.Embedding(1024, 768)
        self.layout_embedding = nn.Linear(6, 768)  # x0,y0,x1,y1,w,h

        self.transformer = nn.TransformerEncoder(
            num_layers=12,
            d_model=768,
            nhead=12
        )

    def forward(self, tokens, bboxes, image):
        """
        Process document with text, layout, and visual information.

        Args:
            tokens: Text tokens
            bboxes: Bounding boxes for each token
            image: Document image

        Returns:
            Contextual embeddings
        """
        # Text embeddings
        text_embed = self.text_embedding(tokens)

        # Positional embeddings
        positions = torch.arange(len(tokens))
        pos_embed = self.position_embedding(positions)

        # Layout embeddings (spatial coordinates)
        layout_embed = self.layout_embedding(bboxes)

        # Visual embeddings from image regions
        visual_embed = self.extract_visual_features(image, bboxes)

        # Combine all modalities
        combined = text_embed + pos_embed + layout_embed + visual_embed

        # Transformer processing
        output = self.transformer(combined)

        return output

# Contextual document understanding

class ContextualOCR:
    """OCR with document-level context understanding."""

    def __init__(self):
        self.document_classifier = DocumentTypeClassifier()
        self.knowledge_base = DocumentKnowledgeBase()
        self.ocr_engine = AdaptiveOCREngine()

    def process_with_context(self, image):
        """Process document using contextual understanding."""

        # 1. Identify document type
        doc_type = self.document_classifier.classify(image)
        # Output: "invoice", "medical_form", "legal_contract", etc.

        # 2. Retrieve domain knowledge
        schema = self.knowledge_base.get_schema(doc_type)
        # Expected fields, formats, validation rules

        # 3. Context-aware OCR
        raw_text = self.ocr_engine.extract_text(
            image,
            expected_structure=schema
        )

        # 4. Semantic validation
        validated = self.validate_against_schema(raw_text, schema)

        # 5. Error correction with domain knowledge
        corrected = self.apply_domain_corrections(
            validated,
            doc_type=doc_type
        )

        return corrected

    def apply_domain_corrections(self, text, doc_type):
        """Apply domain-specific error correction."""

        if doc_type == "invoice":
            # Validate invoice numbers, amounts, dates
            text = self.correct_invoice_fields(text)

        elif doc_type == "medical_form":
            # Correct medication names, dosages
            text = self.correct_medical_terms(text)

        elif doc_type == "legal_contract":
            # Validate legal terminology
            text = self.correct_legal_language(text)

        return text

# Knowledge-enhanced OCR

class KnowledgeEnhancedOCR:
    """OCR augmented with world knowledge."""

    def __init__(self):
        self.ocr = BaseOCREngine()
        self.knowledge_graph = load_knowledge_graph()
        self.llm = load_language_model()

    def process_with_knowledge(self, image):
        """Process using external knowledge."""

        # Initial OCR
        raw_text = self.ocr.extract_text(image)

        # Extract entities
        entities = self.extract_entities(raw_text)

        # Enrich with knowledge graph
        for entity in entities:
            # Look up in knowledge base
            knowledge = self.knowledge_graph.lookup(entity)

            # Use knowledge to resolve ambiguities
            if knowledge:
                entity['type'] = knowledge['type']
                entity['canonical_form'] = knowledge['canonical_name']
                entity['context'] = knowledge['description']

        # Use LLM for intelligent error correction
        corrected_text = self.llm.correct_with_context(
            raw_text,
            entities=entities,
            task="correct OCR errors using world knowledge"
        )

        return corrected_text

# Self-supervised document pre-training

class DocumentImageTransformer:
    """Self-supervised pre-training for document understanding."""

    def pretrain(self, unlabeled_documents):
        """
        Pre-train on unlabeled documents using masked image modeling.
        """
        for document_batch in unlabeled_documents:
            # Randomly mask image patches
            masked_doc, mask = self.apply_random_masking(
                document_batch,
                mask_ratio=0.75
            )

            # Predict masked regions
            predicted = self.model(masked_doc)

            # Reconstruction loss
            loss = self.compute_reconstruction_loss(
                predicted,
                original=document_batch,
                mask=mask
            )

            # Backpropagation
            loss.backward()
            self.optimizer.step()

        return self.model

# Few-shot learning for new document types

class FewShotDocumentLearner:
    """Adapt to new document types with few examples."""

    def __init__(self):
        self.base_model = load_pretrained_model("document_foundation")

    def adapt(self, support_set, query_document):
        """
        Adapt model using few examples (support set).

        Args:
            support_set: 5-10 labeled examples of new document type
            query_document: Unlabeled document to process

        Returns:
            Processed document
        """
        # Extract prototypical representations from support set
        prototypes = self.compute_prototypes(support_set)

        # Compute similarity between query and prototypes
        similarities = self.compute_similarities(
            query_document,
            prototypes
        )

        # Weighted prediction based on prototype matching
        prediction = self.weighted_predict(
            query_document,
            similarities,
            prototypes
        )

        return prediction

    def compute_prototypes(self, support_set):
        """Compute class prototypes from support examples."""
        prototypes = {}

        for label, examples in support_set.items():
            # Encode all examples
            encodings = [self.base_model.encode(ex) for ex in examples]

            # Average to create prototype
            prototypes[label] = torch.mean(torch.stack(encodings), dim=0)

        return prototypes

# Multimodal document reasoning

class DocumentReasoningSystem:
    """Advanced reasoning over document content."""

    def __init__(self):
        self.vision_model = load_vision_model()
        self.reasoning_model = load_reasoning_model()

    def answer_complex_question(self, documents, question):
        """
        Answer questions requiring reasoning over multiple documents.

        Example: "What is the total cost of all items marked 'urgent'
                 across all invoices from Q3 2024?"
        """
        # 1. Parse each document
        parsed_docs = [self.vision_model.parse(doc) for doc in documents]

        # 2. Build structured representation
        structured = self.build_knowledge_graph(parsed_docs)

        # 3. Decompose question into sub-questions
        sub_questions = self.reasoning_model.decompose_query(question)
        # ["Which invoices are from Q3 2024?",
        #  "Which items are marked urgent?",
        #  "What are the costs?",
        #  "Sum the costs"]

        # 4. Answer each sub-question
        sub_answers = []
        for sq in sub_questions:
            answer = self.reasoning_model.answer(sq, structured)
            sub_answers.append(answer)

        # 5. Combine into final answer
        final_answer = self.reasoning_model.synthesize(
            sub_answers,
            original_question=question
        )

        return final_answer

# Continuous learning from user feedback

class ContinuousLearningOCR:
    """OCR that improves from user corrections."""

    def __init__(self):
        self.model = load_base_model()
        self.correction_buffer = []
        self.update_threshold = 100  # Update after 100 corrections

    def process_with_feedback(self, image, user_corrections=None):
        """Process document and incorporate user feedback."""

        # Initial prediction
        prediction = self.model.predict(image)

        if user_corrections:
            # Store correction example
            self.correction_buffer.append({
                'image': image,
                'prediction': prediction,
                'correction': user_corrections
            })

            # Check if enough corrections accumulated
            if len(self.correction_buffer) >= self.update_threshold:
                self.update_model()

        return prediction

    def update_model(self):
        """Update model using accumulated corrections."""

        # Create fine-tuning dataset from corrections
        train_data = self.prepare_training_data(self.correction_buffer)

        # Fine-tune model
        self.model.fine_tune(
            train_data,
            epochs=3,
            learning_rate=1e-5
        )

        # Clear buffer
        self.correction_buffer = []

        logger.info("Model updated with user corrections")

# Future multimodal document API (conceptual)

import document_ai  # Hypothetical future API

client = document_ai.Client(api_key="...")

# Upload document
result = client.understand_document(
    file_path="contract.pdf",
    tasks=[
        "extract_text",
        "identify_parties",
        "find_obligations",
        "detect_risks",
        "summarize_terms"
    ],
    output_format="structured_json"
)

# Result contains rich understanding
print(result.summary)
print(result.parties)  # Automatically identified
print(result.obligations)  # Contractual obligations extracted
print(result.risk_score)  # AI-assessed risk level

# Explainable OCR output

class ExplainableOCR:
    """OCR with interpretable outputs."""

    def process_with_explanation(self, image):
        """Process and explain decisions."""

        result = self.model.predict(image)

        explanation = {
            'text': result.text,
            'confidence': result.confidence,
            'reasoning': {
                'character_level': self.explain_characters(image, result),
                'word_level': self.explain_words(image, result),
                'document_level': self.explain_structure(image, result)
            },
            'uncertainty': self.quantify_uncertainty(result),
            'alternatives': self.generate_alternatives(image, top_k=3)
        }

        return explanation

    def explain_characters(self, image, result):
        """Explain character-level decisions."""
        return {
            'attention_maps': self.model.get_attention_maps(),
            'alternative_readings': self.get_character_alternatives(),
            'confidence_scores': self.get_character_confidences()
        }