What problems could text recognition (OCR) solve?

Text recognition (OCR) solves problems related to extracting and digitizing text from images or physical documents, enabling automated processing and analysis. OCR converts unstructured visual data—like scanned papers, photos, or handwritten notes—into machine-readable text, which can then be integrated into software systems. This capability addresses challenges in data entry, accessibility, and workflow automation across industries.

One major application is reducing manual data entry and human error. For example, businesses processing invoices, receipts, or forms often rely on employees to manually transcribe information into databases or accounting systems. OCR automates this by extracting text from scanned documents, saving time and minimizing typos. In healthcare, patient records stored as scanned PDFs can be converted into searchable digital formats, allowing clinics to quickly retrieve information without manually sifting through files. Similarly, logistics companies use OCR to read shipping labels or tracking numbers from images, streamlining package sorting and delivery updates.

OCR also improves accessibility and information retrieval. Scanned books or historical archives, which are essentially images of text, become searchable and editable when processed with OCR. Libraries and universities use this to digitize rare manuscripts, making them accessible online. For visually impaired users, OCR paired with text-to-speech tools can read aloud text from product packaging or street signs captured via smartphone cameras. Additionally, OCR enables searching within scanned PDFs—a common pain point for professionals managing large document repositories. Instead of relying on metadata or manual tagging, users can directly search for keywords within the document’s content.

Finally, OCR supports automation in modern software systems. Mobile banking apps, for instance, use OCR to extract account numbers and amounts from check photos for remote deposits. Governments automate passport or ID verification by reading text from uploaded images. Retailers analyze customer feedback by scraping text from social media images or survey forms. Developers can integrate OCR APIs—like Google’s Vision AI or Tesseract—into apps to process user-uploaded images, extract text, and trigger actions like filing expenses or updating records. This reduces reliance on manual intervention and accelerates data-driven decision-making. By bridging the gap between physical documents and digital workflows, OCR unlocks efficiency in scenarios where text exists outside structured databases.