OCR Boosting Module for Character Recognition Accuracy

The system performs preliminary actions by creating a learn set from initial OCR results before final recognition. It generates character imagelets, clusters them using statistical methods, and creates reference patterns in advance. This preliminary processing establishes a customized vocabulary and character models that improve subsequent OCR accuracy without requiring complex real-time processing during the actual recognition phase.

The patent introduces an intermediary component - the OCR boosting module with learn set - that acts as a mediator between the traditional OCR system and the final output. This intermediary processes initial OCR results, clusters character imagelets, and provides corrected reference patterns back to the OCR system, thereby improving accuracy without replacing the entire OCR pipeline.

The system performs the computationally intensive clustering and learn set creation as a preliminary offline process. By pre-processing document images, extracting character imagelets, and clustering them into reference patterns before the actual OCR task, the system avoids repeating these expensive operations during real-time processing, thus reducing the time loss during critical recognition phases.

The OCR boosting module performs self-service by automatically clustering character imagelets and generating its own learn set without requiring manual intervention. The system uses statistical measurements and automated clustering algorithms to create reference patterns from initial OCR results, making the process self-sufficient and reducing the need for manual parameter tuning or extensive processing during operation.

Instead of using multiple separate OCR modules, the system changes parameters within a single OCR framework by creating a customized learn set with clustered character imagelets. The clustering process generates reference patterns that capture variations in fonts, scales, and styles, allowing the OCR system to adapt to different document types by adjusting its reference patterns rather than deploying multiple specialized modules.

The OCR boosting module serves multiple functions: it clusters character imagelets, creates reference patterns, generates customized vocabularies, and provides correction feedback. This single multi-functional component replaces what would otherwise require multiple specialized OCR modules, reducing system complexity while maintaining adaptability to handle various font variations and document types.