357 lines
8.2 KiB
Markdown
357 lines
8.2 KiB
Markdown
---
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name: pdf-text-extractor
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description: "使用OCR支持从PDF中提取文本。"
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metadata:
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{
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"openclaw":
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{
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"version": "1.0.0",
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"author": "Vernox",
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"license": "MIT",
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"tags": ["pdf", "ocr", "text", "extraction", "document", "digitization"],
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"category": "tools"
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}
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}
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---
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# PDF-Text-Extractor - Extract Text from PDFs
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**Vernox Utility Skill - Perfect for document digitization.**
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## Overview
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PDF-Text-Extractor is a zero-dependency tool for extracting text content from PDF files. Supports both embedded text extraction (for text-based PDFs) and OCR (for scanned documents).
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## Features
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### ✅ Text Extraction
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- Extract text from PDFs without external tools
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- Support for both text-based and scanned PDFs
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- Preserve document structure and formatting
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- Fast extraction (milliseconds for text-based)
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### ✅ OCR Support
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- Use Tesseract.js for scanned documents
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- Support multiple languages (English, Spanish, French, German)
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- Configurable OCR quality/speed
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- Fallback to text extraction when possible
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### ✅ Batch Processing
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- Process multiple PDFs at once
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- Batch extraction for document workflows
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- Progress tracking for large files
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- Error handling and retry logic
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### ✅ Output Options
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- Plain text output
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- JSON output with metadata
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- Markdown conversion
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- HTML output (preserving links)
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### ✅ Utility Features
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- Page-by-page extraction
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- Character/word counting
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- Language detection
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- Metadata extraction (author, title, creation date)
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## Installation
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```bash
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clawhub install pdf-text-extractor
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```
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## Quick Start
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### Extract Text from PDF
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```javascript
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const result = await extractText({
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pdfPath: './document.pdf',
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options: {
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outputFormat: 'text',
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ocr: true,
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language: 'eng'
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}
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});
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console.log(result.text);
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console.log(`Pages: ${result.pages}`);
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console.log(`Words: ${result.wordCount}`);
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```
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### Batch Extract Multiple PDFs
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```javascript
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const results = await extractBatch({
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pdfFiles: [
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'./document1.pdf',
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'./document2.pdf',
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'./document3.pdf'
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],
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options: {
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outputFormat: 'json',
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ocr: true
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}
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});
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console.log(`Extracted ${results.length} PDFs`);
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```
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### Extract with OCR
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```javascript
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const result = await extractText({
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pdfPath: './scanned-document.pdf',
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options: {
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ocr: true,
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language: 'eng',
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ocrQuality: 'high'
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}
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});
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// OCR will be used (scanned document detected)
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```
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## Tool Functions
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### `extractText`
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Extract text content from a single PDF file.
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**Parameters:**
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- `pdfPath` (string, required): Path to PDF file
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- `options` (object, optional): Extraction options
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- `outputFormat` (string): 'text' | 'json' | 'markdown' | 'html'
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- `ocr` (boolean): Enable OCR for scanned docs
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- `language` (string): OCR language code ('eng', 'spa', 'fra', 'deu')
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- `preserveFormatting` (boolean): Keep headings/structure
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- `minConfidence` (number): Minimum OCR confidence score (0-100)
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**Returns:**
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- `text` (string): Extracted text content
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- `pages` (number): Number of pages processed
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- `wordCount` (number): Total word count
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- `charCount` (number): Total character count
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- `language` (string): Detected language
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- `metadata` (object): PDF metadata (title, author, creation date)
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- `method` (string): 'text' or 'ocr' (extraction method)
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### `extractBatch`
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Extract text from multiple PDF files at once.
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**Parameters:**
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- `pdfFiles` (array, required): Array of PDF file paths
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- `options` (object, optional): Same as extractText
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**Returns:**
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- `results` (array): Array of extraction results
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- `totalPages` (number): Total pages across all PDFs
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- `successCount` (number): Successfully extracted
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- `failureCount` (number): Failed extractions
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- `errors` (array): Error details for failures
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### `countWords`
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Count words in extracted text.
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**Parameters:**
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- `text` (string, required): Text to count
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- `options` (object, optional):
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- `minWordLength` (number): Minimum characters per word (default: 3)
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- `excludeNumbers` (boolean): Don't count numbers as words
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- `countByPage` (boolean): Return word count per page
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**Returns:**
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- `wordCount` (number): Total word count
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- `charCount` (number): Total character count
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- `pageCounts` (array): Word count per page
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- `averageWordsPerPage` (number): Average words per page
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### `detectLanguage`
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Detect the language of extracted text.
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**Parameters:**
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- `text` (string, required): Text to analyze
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- `minConfidence` (number): Minimum confidence for detection
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**Returns:**
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- `language` (string): Detected language code
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- `languageName` (string): Full language name
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- `confidence` (number): Confidence score (0-100)
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## Use Cases
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### Document Digitization
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- Convert paper documents to digital text
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- Process invoices and receipts
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- Digitize contracts and agreements
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- Archive physical documents
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### Content Analysis
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- Extract text for analysis tools
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- Prepare content for LLM processing
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- Clean up scanned documents
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- Parse PDF-based reports
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### Data Extraction
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- Extract data from PDF reports
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- Parse tables from PDFs
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- Pull structured data
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- Automate document workflows
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### Text Processing
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- Prepare content for translation
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- Clean up OCR output
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- Extract specific sections
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- Search within PDF content
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## Performance
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### Text-Based PDFs
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- **Speed:** ~100ms for 10-page PDF
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- **Accuracy:** 100% (exact text)
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- **Memory:** ~10MB for typical document
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### OCR Processing
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- **Speed:** ~1-3s per page (high quality)
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- **Accuracy:** 85-95% (depends on scan quality)
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- **Memory:** ~50-100MB peak during OCR
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## Technical Details
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### PDF Parsing
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- Uses native PDF.js library
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- Extracts text layer directly (no OCR needed)
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- Preserves document structure
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- Handles password-protected PDFs
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### OCR Engine
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- Tesseract.js under the hood
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- Supports 100+ languages
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- Adjustable quality/speed tradeoff
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- Confidence scoring for accuracy
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### Dependencies
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- **ZERO external dependencies**
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- Uses Node.js built-in modules only
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- PDF.js included in skill
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- Tesseract.js bundled
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## Error Handling
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### Invalid PDF
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- Clear error message
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- Suggest fix (check file format)
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- Skip to next file in batch
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### OCR Failure
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- Report confidence score
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- Suggest rescan at higher quality
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- Fallback to basic extraction
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### Memory Issues
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- Stream processing for large files
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- Progress reporting
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- Graceful degradation
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## Configuration
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### Edit `config.json`:
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```json
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{
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"ocr": {
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"enabled": true,
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"defaultLanguage": "eng",
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"quality": "medium",
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"languages": ["eng", "spa", "fra", "deu"]
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},
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"output": {
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"defaultFormat": "text",
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"preserveFormatting": true,
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"includeMetadata": true
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},
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"batch": {
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"maxConcurrent": 3,
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"timeoutSeconds": 30
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}
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}
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```
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## Examples
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### Extract from Invoice
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```javascript
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const invoice = await extractText('./invoice.pdf');
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console.log(invoice.text);
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// "INVOICE #12345 Date: 2026-02-04..."
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```
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### Extract from Scanned Contract
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```javascript
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const contract = await extractText('./scanned-contract.pdf', {
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ocr: true,
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language: 'eng',
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ocrQuality: 'high'
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});
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console.log(contract.text);
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// "AGREEMENT This contract between..."
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```
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### Batch Process Documents
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```javascript
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const docs = await extractBatch([
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'./doc1.pdf',
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'./doc2.pdf',
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'./doc3.pdf',
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'./doc4.pdf'
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]);
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console.log(`Processed ${docs.successCount}/${docs.results.length} documents`);
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```
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## Troubleshooting
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### OCR Not Working
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- Check if PDF is truly scanned (not text-based)
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- Try different quality settings (low/medium/high)
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- Ensure language matches document
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- Check image quality of scan
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### Extraction Returns Empty
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- PDF may be image-only
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- OCR failed with low confidence
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- Try different language setting
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### Slow Processing
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- Large PDF takes longer
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- Reduce quality for speed
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- Process in smaller batches
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## Tips
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### Best Results
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- Use text-based PDFs when possible (faster, 100% accurate)
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- High-quality scans for OCR (300 DPI+)
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- Clean background before scanning
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- Use correct language setting
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### Performance Optimization
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- Batch processing for multiple files
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- Disable OCR for text-based PDFs
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- Lower OCR quality for speed when acceptable
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## Roadmap
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- [ ] PDF/A support
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- [ ] Advanced OCR pre-processing
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- [ ] Table extraction from OCR
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- [ ] Handwriting OCR
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- [ ] PDF form field extraction
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- [ ] Batch language detection
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- [ ] Confidence scoring visualization
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## License
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MIT
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---
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**Extract text from PDFs. Fast, accurate, zero dependencies.** 🔮
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