memory-bank/productContext.md

Product Context: HOCR Processing Tool

1. Problem Space

• Extracting text from images and scanned documents (PDFs) is a common but often challenging task.
• Documents vary widely in quality, layout, language, and format.
• Manual transcription is time-consuming, error-prone, and not scalable.
• Existing OCR tools may lack flexibility, specific preprocessing capabilities, or produce output that isn't easily usable for downstream tasks.

2. Solution Vision

• Provide a reliable, configurable, and extensible OCR processing pipeline.
• Empower users to convert document images and PDFs into usable, structured text data.
• Offer fine-grained control over the OCR process, from preprocessing to output formatting.
• Serve as a foundational tool that can be integrated into larger document processing workflows.

3. Target Audience & Needs

• Researchers/Academics: Need to extract text from historical documents, scanned books, or research papers for analysis. Require high accuracy and potentially language-specific handling.
• Archivists/Librarians: Need to digitize large collections of documents, making them searchable and accessible. Require batch processing capabilities and robust handling of varied document types.
• Developers: Need an OCR component to integrate into applications (e.g., document management systems, data extraction tools). Require a clear API or command-line interface and structured output (like JSON or hOCR).
• General Users: May need to occasionally extract text from a scanned form, receipt, or image. Require a simple interface (if applicable) and reasonable default settings.

4. User Experience Goals

• Accuracy: The primary goal is to maximize the accuracy of the extracted text.
• Configurability: Users should be able to tailor the processing steps and parameters to their specific document types and needs.
• Transparency: The tool should provide feedback on the processing steps and allow for debugging (e.g., viewing intermediate images, logs).
• Performance: Processing should be reasonably efficient, especially for batch operations.
• Ease of Use: While powerful, the tool should be approachable, whether through a command-line interface or a potential GUI. Configuration should be clear and well-documented.

5. How it Should Work (High-Level Flow)

1. User provides input file(s) (image or PDF).
2. User specifies configuration options (or uses defaults).
3. The tool executes the configured pipeline:
   • Preprocessing (optional steps like deskew, binarize).
   • Segmentation (detecting text regions).
   • OCR (applying Tesseract or another engine).
   • Post-processing (e.g., text correction, structuring output).
4. The tool outputs the extracted text in the desired format (plain text, hOCR, JSON).
5. Logs and potentially intermediate results are generated for review.

(This context provides the 'why' and 'how' from a user perspective. Technical details are in systemPatterns.md and techContext.md.)