OCR a. Finishing work on the previous models Finishing research for R&D-level solutions that were developed during the first phase of the project. Assessment of validity and potential improvements to the solutions.
OCR. 5 1. Whenever possible, problems shall be resolved informally and at the point of service. 6 CONTRACTOR shall establish an internal informal problem resolution process for clients not able to 7 resolve such problems at the point of service. Clients may initiate a grievance or complaint directly with 8 CONTRACTOR either orally or in writing.
OCR. “OCR” shall mean the Office for Civil Rights (who oversees and enforces HIPAA and HITECH).
OCR. Optical character recognition software provided under this Agreement that is used to automatically extract characters from an image of a license plate.
OCR. 2 shall advise clients of the following: CONTRACTOR’s statement
OCR. CONTRACTOR’s statement shall advise participants of the following:
OCR optical character recognition, technology used at some terminals to read the number plate and record the state of ITUs entering the terminal by road.
OCR. 484-C – Each month, the CONSULTANT shall submit this Form to the MDOT Consultant Services Unit (CSU) along with the Invoice. This form certifies payments to all Subcontractors and shows all firms even if the CONSULTANT has paid no monies to the firm during that estimate period (negative report).
OCR. Optical Character Recognition is a technique used to identify glyphs within scanned documents or images, and to produce the appropriate electronically encoded characters, usually in ASCII or in Unicode [63]. This is often combined with other techniques to produce further analysis, such as identifying words, formulae, tables, diagrams and other structural elements within the document. The most highly researched area of OCR is the recognition of plain text written in a Latin script, and there are many high quality commercial and open source software applications available, which when used in conjunction with high quality documents, can achieve extremely high recognition rates. In this case, a high quality document would have a number of attributes, namely ∙ Well printed, on a good paper, with glyphs made of solid unbroken lines. ∙ Well typeset, in a common font with no overlapping of characters, consistent spacing. ∙ Well scanned, at a resolution of around 600 DPI or above, with minimal noise and skew. However, documents frequently lack some of these attributes and many users require more than just plain text recognition, for example, the identification and analysis of elements such as tables, formulae and metadata. These tasks require more advanced techniques and are the focus of much current research. There are several OCR tools available, either free (OCRopus, Tesseract , Ocrad , GOCR or CuneiForm) or commercial (FineReader, Omnipage, AnyDoc Software, Brain- xxxx, ExperVision, Readiris, ReadSoft , SmartScore, Infty or Simple OCR ).1
