OCR (Optical Character Recognition) data extraction involves converting text from scanned images, documents, or PDFs into machine-readable formats. The process begins by detecting text regions within an image and recognizing characters using OCR algorithms. Modern OCR systems, often powered by deep learning, can handle diverse fonts, languages, and even handwritten text. Extracted text is typically organized into structured formats, such as tables or JSON files, for further processing. Applications include digitizing invoices, automating form data entry, and enabling searchable document archives. OCR data extraction improves efficiency and accuracy in text processing workflows.
What's OCR data extraction?
Keep Reading
I'm getting poor results when using a Sentence Transformer on domain-specific text (like legal or medical documents) — how can I improve the model's performance on that domain?
To improve Sentence Transformers on domain-specific text, focus on adapting the model to your domain through fine-tuning
How do GANs generate images or videos?
Generative Adversarial Networks (GANs) generate images or videos through a process involving two networks: a generator a
How does indexing affect full-text search performance?
Indexing plays a crucial role in improving full-text search performance by allowing the search engine to quickly locate