OCRprocessed

OCRprocessed refers to the state of a document after it has been processed by an optical character recognition (OCR) system. In typical workflows, raw scanned images are fed to OCR software, which outputs OCRprocessed data consisting of machine-readable text, layout information, and metadata that describe the document's structure and provenance. The term distinguishes the processed results from the original image and from intermediate OCR attempts.

Components of OCRprocessed data usually include recognized text with character and word bounding boxes, along with confidence scores. Layout analysis identifies zones such as headers, paragraphs, and tables, and determines reading order. Language identifiers may be included, as well as optional post-processing steps like spell checking, normalization, and error correction. In many pipelines, OCRprocessed data also carries metadata about the source, page dimensions, and bounding coordinates for downstream processing.

Formats commonly used to encode OCRprocessed output include HOCR, PAGE XML, ALTO, and JSON. These formats preserve positional data and structural information, enabling indexing, search, and automated extraction of structured data from forms, invoices, and other documents.

Applications for OCRprocessed data span digitization projects, accessibility initiatives, searchable archival repositories, and automated data extraction workflows. It also serves as a precursor to downstream natural language processing and document understanding tasks.

Quality and challenges: OCRprocessed quality is assessed via metrics such as character error rate and word error rate, as well as layout accuracy. Challenges include noisy or degraded scans, skew, diverse fonts, handwriting, multilingual content, and complex layouts, all of which can impact accuracy and usability.