This text discusses the challenges and considerations of OCR (Optical Character Recognition) for digital data. It highlights the problems with OCR, such as the need for perfect recognition and the confusion caused by similar characters. It explores various encoding schemes like base64, base32, and hexadecimal, and their compatibility with OCR. The text also presents successful OCR configurations for different encoding schemes, including base16 and bip39. It mentions the importance of factors like OCR engines, font type, scanning quality, and paper size. Overall, the text emphasizes the difficulty of OCR for digital data and provides insights into optimizing OCR processes.
| Signal | Change | 10y horizon | Driving force |
|---|---|---|---|
| Printing digital data on paper | Storing digital data on paper | More efficient and accurate OCR technology | Need for secure and reliable data storage |
| Challenges with OCR of digital data | Need for 100% accurate recognition in OCR | Improved OCR algorithms and technology | Demand for reliable and error-free OCR |
| OCR of hexadecimal data with 100% accuracy | Successful recognition of base16 data | More accurate OCR engines and optimized settings | Advances in OCR technology and algorithms |
| OCR of BIP39 data with 100% accuracy | Successful recognition of bip39 data | Increased compatibility of OCR engines with bip39 encoding | Growing adoption of bip39 encoding |
| OCR of base64 data with 100% post-correction | Successful recognition of base64 data with errors | Development of error-correction algorithms for base64 OCR | Need for accurate OCR of base64 data |
| OCR of base32 data with low error rate | Improved recognition accuracy for base32 data | Enhanced OCR algorithms and optimized settings | Demand for accurate OCR of base32 data |
| OCR with error-correction | Use of error-correcting codes in OCR | Integration of error-correction codes in OCR algorithms | Need for reliable and accurate OCR |
| Voting-based OCR system | Utilizing multiple OCR engines for better accuracy | Collaborative approach in OCR technology development | Improvement of OCR accuracy through collaboration |
| OCR for source code with post-processing | Repairing OCR errors in source code with checksums | Development of post-processing techniques for OCR errors | Need for accurate OCR of source code |
| Long-term archiving of digital data on microfilm | Storing base64 and paperback data on microfilm | Improved OCR accuracy for base64 and paperback data | Preservation and archiving of digital data |
| Paper augmented digital documents | Formats suitable for both humans and computers | Development of document formats compatible with OCR | Enhanced usability and accessibility of documents |
| OCR of DSLs for software models | Recognition of software models in DSLs | Improved OCR algorithms for recognizing formal alphabets | Efficient extraction of software models from DSLs |
| Finding optimal fonts for OCR engines | Identifying fonts with high OCR accuracy | Improved font selection algorithms for OCR engines | Optimization of OCR performance with specific fonts |
| Finding optimal base32 alphabets for OCR | Creating tailored alphabets for optimal OCR results | Enhanced algorithms for generating optimized alphabets | Optimization of OCR performance with specific alphabets |