How is OCR technology limited in comparing schemas?
Best insight from top research papers
OCR technology faces limitations when comparing schemas due to its focus on text extraction from images rather than semantic understanding of data structures. While OCR excels at recognizing and retrieving text information, it lacks the ability to comprehend the underlying structure and relationships within the extracted text. This limitation becomes evident when attempting to compare schemas, as OCR tools primarily focus on character and word detection rather than understanding the hierarchical organization and connections between different elements in a schema. Additionally, OCR evaluation metrics often do not consider the accuracy of layout analysis, which is crucial for understanding the schema's layout and reading order. Therefore, OCR technology may struggle to provide comprehensive schema comparisons due to its primary function of text extraction without deep semantic understanding of data structures.
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| Papers (5) | Insight |
|---|---|
18 Sep 2011 | OCR technology is limited in applying frequency-based language models due to potential errors. Noisy-channel models are needed to address classifier and segmentation errors for improved accuracy. |
14 Mar 2023 | Not addressed in the paper. |
| OCR technology is limited in comparing schemas due to variations in recognition accuracy among different OCR methods like GCV, Tesseract, Abbyy Finereader, and Transym OCR, impacting performance consistency. | |
| OCR technology is limited in comparing schemas due to varying OCR evaluation tools, metrics, and layout analysis accuracy, hindering direct comparisons of results across different implementations. | |
| ORMapping technology, not OCR, bridges ontology and relation schemas. OCR is not discussed in the paper. "Not addressed in the paper." |
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