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Optical character recognition (OCR) or optical character reader is the electronic or mechanical conversion of images of typed, handwritten or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a scene photo (for example the text on signs and billboards in a landscape photo) or from subtitle text superimposed…
History, Works & Applications
Explore the main themes, entities and connections around Optical character recognition. Start with the topic map, then use the sections below for research and deeper semantic analysis.
Start with a few of the strongest sections from the source topic. These are research directions, not a list of keywords you must use.
High-confidence facts extracted from structured source data. Use them as anchors for further research.
Browse the full topic structure. Each item opens a new analysis centered on that subject.
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
See the strongest relationship patterns around the current topic before diving into the raw triples.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
ocr recognition character text image used accuracy characters printed images software example documents one words document fonts glyph cursive optical
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| cognitive computing | instance of | and used in machine processes | 0.80 | text |
| machine translation | instance of | and used in machine processes | 0.80 | text |
| receipts | instance of | ApplicationsOCR engines have been developed into software applications specializing in various subjects | 0.80 | text |
| invoices | instance of | ApplicationsOCR engines have been developed into software applications specializing in various subjects | 0.80 | text |
| checks | instance of | ApplicationsOCR engines have been developed into software applications specializing in various subjects | 0.80 | text |
| and legal billing documents.The software can be used for | instance of | ApplicationsOCR engines have been developed into software applications specializing in various subjects | 0.80 | text |
| Cuneiform | instance of | Nearest neighbour classifiers such as the k-nearest neighbors algorithm are used to compare image features with stored glyph features and choose the nearest match.Software | 0.80 | text |
| Tesseract use a two-pass approach to character recognition | instance of | Nearest neighbour classifiers such as the k-nearest neighbors algorithm are used to compare image features with stored glyph features and choose the nearest match.Software | 0.80 | text |
| Arial or Times New Roman | instance of | Several prominent OCR engines were designed to capture text in popular fonts | 0.80 | text |
| and are incapable of capturing text in these fonts that are specialized | instance of | Several prominent OCR engines were designed to capture text in popular fonts | 0.80 | text |
| very different from popularly used fonts | instance of | Several prominent OCR engines were designed to capture text in popular fonts | 0.80 | text |
| Optical character recognition | related to External links | Unicode OCR | 0.60 | section |
These clusters group vocabulary that occurs around closely connected concepts in the source material.
Bridges can reveal useful research angles that are easy to miss in a flat list of related terms.