Non-machine-readable

Describing data or information that cannot be directly processed and interpreted by a computer or electronic device without human intervention or specialized processing. This typically applies to formats such as handwritten documents, images of text (e.g., scanned documents or photos of signs), or audio recordings. The lack of machine readability means the information's structure isn't pre-defined or encoded in a way a computer can readily parse and utilize. Converting non-machine-readable data requires processes like optical character recognition (OCR) for images, transcription for audio, or manual data entry. It often presents challenges for automation, data analysis, and digital accessibility. It contrasts significantly with machine-readable formats like databases, spreadsheets, and encoded text files.

Non-machine-readable meaning with examples

• The historical society's archives contain many non-machine-readable documents, such as handwritten letters and photographs, making it difficult to efficiently search and analyze their content. Researchers need to manually transcribe the letters and describe the images to unlock their value, a time-consuming process that limits the scope of their investigations. Without converting these to machine readable, there's little that can be gleaned.
• During a recent audit, a significant portion of the invoices were found to be non-machine-readable, scanned images rather than digital files. This issue slowed down the automated accounting system and required manual review, leading to increased labor costs and potential for errors, which also makes it challenging for any data mining process. Conversion into machine-readable data is required.
• The census data from the 18th century is largely non-machine-readable, comprised of handwritten records that require specialized expertise to interpret and translate into a digital format. Historians must painstakingly transcribe each entry and build digital databases, which is resource intensive but essential for demographic studies. Making them machine readable would allow broader data analyses.
• The company's initial CRM system relied heavily on non-machine-readable handwritten notes. Employees struggle to access important client information for seamless customer service. The lack of a standardized structure, meaning digital and structured, makes it hard to analyze customer data effectively or provide targeted marketing, and creates potential losses.
• In the context of healthcare, medical records that are non-machine-readable, such as scanned documents or faxes, can create bottlenecks in the delivery of care and information exchange. Doctors and nurses will need to manually enter data or copy it, which increases risk of transcription errors. The lack of standardization makes it challenging for automated systems to interact.