PDF and Image Text Extraction

Problems It Solves

Extracting text from PDFs and images often suffers from low fidelity. Scanned PDFs with missing text layers lead to information loss; mixed PDFs have inconsistent text layers and visual content; complex structures like multi-column layouts or tables become garbled; and critical numbers, amounts, or IDs are prone to recognition errors. Traditional tools either ignore document structure or apply a one-size-fits-all approach, failing to select optimal strategies for different document types and user intents.

How the Skill Works

This skill addresses these problems through a systematic workflow with core capabilities:

• File Type Diagnosis: Performs page-level analysis on PDFs, calculating text layer metrics to classify pages as native-text, scanned, mixed, or suspicious; for images, checks resolution, orientation, etc.
• Recognition Path Selection: Native PDFs prioritize text layer extraction; scanned pages or images trigger visual/OCR paths; mixed types combine both, supplementing recognition for missing areas.
• Structure Recovery: Restores reading order using coordinates, line spacing, font size, and other signals to handle headings, lists, and tables. For example, multi-column pages are read in "column-by-column, top-to-bottom" order; tables first identify headers and row/column boundaries.
• High-Risk Content Verification: Conducts multi-round checks on critical information like dates, amounts, and IDs, using markers such as [?] for uncertain characters, avoiding overwriting visual evidence with common sense.

Key steps are:
1. Page Diagnosis: Retrieves PDF page count, metadata, or analyzes image quality to classify page types.
2. Recognition Execution: Extracts text according to the selected path; for images, explicitly requires output of all visible content in natural reading order.
3. Structure Rebuilding: Synthesizes multiple features to recover paragraph breaks, heading hierarchies, and table layouts, avoiding mechanical concatenation.
4. Quality Control: Before output, verifies page count, reading order, table alignment, etc., using internal status markers like OK_NATIVE or LOW_CONFIDENCE.

Applicability Boundaries and Considerations

• Accuracy Limits: Does not guarantee 100% OCR correctness; focuses on accurate output for confirmed content and explicit marking for uncertainties.
• Document Restrictions: Cannot process encrypted, corrupted, or permission-restricted PDFs; requires users to provide unlocked files.
• Processing Scope: Handles very long PDFs with batch processing to avoid context overload, prioritizing accuracy over one-time processing.
• Output Modes: Offers verbatim transcription, structured Markdown, or data extraction based on user intent, but file generation requires prior validation.
• Quality Assurance: Relies on checklists (e.g., ensuring scanned pages use vision paths, no table column misalignment), but users should watch for anomaly prompts like "Page 7 table columns unclear, with some uncertain numeric fields."