Search results
Results from the WOW.Com Content Network
hOCR is an open standard of data representation for formatted text obtained from optical character recognition (OCR). The definition encodes text, style, layout information, recognition confidence metrics and other information using Extensible Markup Language (XML) in the form of Hypertext Markup Language (HTML) or XHTML.
An example of a traditional OCR use case would be to translate the characters from an image of a printed document, such as a book page, newspaper clipping, or legal contract, into a separate file that could be searched and updated with a word processor or document viewer. It's also quite helpful for automating the processing of forms.
Video of the process of scanning and real-time optical character recognition (OCR) with a portable scanner. Optical character recognition or optical character reader (OCR) 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 ...
Layout analysis software, that divide scanned documents into zones suitable for OCR; Graphical interfaces to one or more OCR engines; Software development kits that are used to add OCR capabilities to other software (e.g. forms processing applications, document imaging management systems, e-discovery systems, records management solutions)
Examples of top-down approaches include the recursive X-Y cut algorithm, which decomposes the document in rectangular sections. [5] There are two issues common to any approach at document layout analysis: noise and skew. Noise refers to image noise, such as salt and pepper noise or Gaussian noise. Skew refers to the fact that a document image ...
The data obtained by this form is regarded as a static representation of handwriting. Offline handwriting recognition is comparatively difficult, as different people have different handwriting styles. And, as of today, OCR engines are primarily focused on machine printed text and ICR for hand "printed" (written in capital letters) text.
An example spangram with corresponding theme words: PEAR, FRUIT, BANANA, APPLE, etc. Need a hint? Find non-theme words to get hints. For every 3 non-theme words you find, you earn a hint.
The standard was initially developed for the description of text OCR and layout information of pages for digitized material. The goal was to describe the layout and text in a form to be able to reconstruct the original appearance based on the digitized information - similar to the approach of a lossless image saving operation.