OCR vs Handwriting Recognition

Developers should learn OCR when building applications that require digitizing printed text, automating document processing, or extracting information from images for data analysis meets developers should learn handwriting recognition when building applications that require natural user interfaces, such as mobile apps with stylus input, educational software for handwriting practice, or systems for digitizing historical documents. Here's our take.

OCR

Nice Pick

Developers should learn OCR when building applications that require digitizing printed text, automating document processing, or extracting information from images for data analysis

Pros

• Common use cases include invoice processing, receipt scanning, license plate recognition, digitizing historical archives, and creating accessible content for visually impaired users by converting text to speech
• Related to: computer-vision, image-processing

Cons

• Specific tradeoffs depend on your use case

Handwriting Recognition

Developers should learn handwriting recognition when building applications that require natural user interfaces, such as mobile apps with stylus input, educational software for handwriting practice, or systems for digitizing historical documents

Pros

• It is particularly useful in industries like finance for check processing, healthcare for prescription digitization, and retail for form automation, where handwritten data needs to be efficiently and accurately converted into machine-readable formats to improve workflow and reduce manual errors
• Related to: machine-learning, computer-vision

Cons

• Specific tradeoffs depend on your use case

The Verdict

These tools serve different purposes. OCR is a tool while Handwriting Recognition is a concept. We picked OCR based on overall popularity, but your choice depends on what you're building.

The Bottom Line

OCR wins

Based on overall popularity. OCR is more widely used, but Handwriting Recognition excels in its own space.