# Fix OCR Track Table Rendering

## Summary

OCR track PDF generation produces tables with incorrect format and layout. Tables appear without proper structure - cell content is misaligned and the visual format differs significantly from the original document. Image placement is correct, but table rendering is broken.

## Problem Statement

When generating PDF from OCR track results (via `scan.pdf` processed by PP-StructureV3), the output tables have:
1. **Wrong cell alignment** - content not positioned in proper cells
2. **Missing table structure** - rows/columns don't match original document layout
3. **Incorrect content distribution** - all content seems to flow linearly instead of maintaining grid structure

Reference: `backend/storage/results/af7c9ee8-60a0-4291-9f22-ef98d27eed52/`
- Original: `af7c9ee8-60a0-4291-9f22-ef98d27eed52_scan_page_1.png`
- Generated: `scan_layout.pdf`
- Result JSON: `scan_result.json` - Tables have correct `{rows, cols, cells}` structure

## Root Cause Analysis

### Issue 1: Table Content Not Converted to TableData Object

In `_json_to_document_element` (pdf_generator_service.py:1952):
```python
element = DocumentElement(
    ...
    content=elem_dict.get('content', ''),  # Raw dict, not TableData
    ...
)
```

Table elements have `content` as a dict `{rows: 5, cols: 4, cells: [...]}` but it's not converted to a `TableData` object.

### Issue 2: OCR Track HTML Conversion Fails

In `convert_unified_document_to_ocr_data` (pdf_generator_service.py:464-467):
```python
elif isinstance(element.content, dict):
    html_content = element.content.get('html', str(element.content))
```

Since there's no 'html' key in the cells-based dict, it falls back to `str(element.content)` = `"{'rows': 5, 'cols': 4, ...}"` - invalid HTML.

### Issue 3: Different Table Rendering Paths

- **Direct track** uses `_draw_table_element_direct` which properly handles dict with cells via `_build_rows_from_cells_dict`
- **OCR track** uses `draw_table_region` which expects HTML strings and fails with dict content

## Proposed Solution

### Option A: Convert dict to TableData during JSON loading (Recommended)

In `_json_to_document_element`, when element type is TABLE and content is a dict with cells, convert it to a `TableData` object:

```python
# For TABLE elements, convert dict to TableData
if elem_type == ElementType.TABLE and isinstance(content, dict) and 'cells' in content:
    content = self._dict_to_table_data(content)
```

This ensures `element.content.to_html()` works correctly in `convert_unified_document_to_ocr_data`.

### Option B: Fix conversion in convert_unified_document_to_ocr_data

Handle dict with cells properly by converting to HTML:

```python
elif isinstance(element.content, dict):
    if 'cells' in element.content:
        # Convert cells-based dict to HTML
        html_content = self._cells_dict_to_html(element.content)
    elif 'html' in element.content:
        html_content = element.content['html']
    else:
        html_content = str(element.content)
```

## Impact on Hybrid Mode

Hybrid mode uses Direct track rendering (`_generate_direct_track_pdf`) which already handles dict content properly via `_build_rows_from_cells_dict`. The proposed fixes should not affect hybrid mode negatively.

However, testing should verify:
1. Hybrid mode continues to work with combined Direct + OCR elements
2. Table rendering quality is consistent across all tracks

## Success Criteria

1. OCR track tables render with correct structure matching original document
2. Cell content positioned in proper grid locations
3. Table borders/grid lines visible
4. No regression in Direct track or Hybrid mode table rendering
5. All test files (scan.pdf, img1.png, img2.png, img3.png) produce correct output

## Files to Modify

1. `backend/app/services/pdf_generator_service.py`
   - `_json_to_document_element`: Convert table dict to TableData
   - `convert_unified_document_to_ocr_data`: Improve dict handling (if Option B)

2. `backend/app/models/unified_document.py` (optional)
   - Add `TableData.from_dict()` class method for cleaner conversion

## Testing Plan

1. Test scan.pdf with OCR track - verify table structure matches original
2. Test img1.png, img2.png, img3.png with OCR track
3. Test PDF files with Direct track - verify no regression
4. Test Hybrid mode with files that trigger OCR fallback