Activation of the gravity sensors in the inner ear—the otoliths—generates reflexes that act to maintain posture and gaze. Ocular counter-rolling (OCR) is an example of such a reflex. When the head is tilted to the side, the eyes rotate around the line of sight in the opposite direction (i.e., counter-rolling). While turning corners, undergoing centrifugation, or making side-to-side tilting head...

Optical Character Recognition, or OCR, is one of the major topics in computer vision technology. It is widely used in various applications, such as a digital libraries, automatic banking systems, and mailing services. Tesseract OCR Engine, which we evaluate in this paper, is one of renowned OCR programs. It was originally developed by Hewlett Packard Lab between 1985 and 1995, and has been main...

This article describes the results of a case study that applies Neural Networkbased Optical Character Recognition (OCR) to scanned images of books printed between 1487 and 1870 by training the OCR engine OCRopus (Breuel et al. 2013) on the RIDGES herbal text corpus (Odebrecht et al. 2017, in press). Training specific OCR models was possible because the necessary ground truth is available as err...

OBJECTIVE The objective was to determine the safety of ocrelizumab (OCR) in patients with rheumatoid arthritis (RA). METHODS This was an analysis of the double-blind, placebo-controlled periods and long-term follow-up of 4 OCR phase III trials in RA (SCRIPT, STAGE, FILM and FEATURE). Safety data per study and the results of a meta-analysis of serious infectious events (SIEs) are presented. ...

Assistant Professor, HIET, Kaithal (Haryana) E-mail: [email protected] 2 Assistant Professor, NIT, Kurukshetra (Haryana) E-mail: [email protected], Assistant Professor, HIET, Kaithal (Haryana) E-mail: [email protected] Assistant Professor, HCTM, Kaithal (Haryana) E-mail: [email protected] Abstract —OCR is the acronym for Optical Character Recognition. This technology allows a machine...

Modern optical character recognition (OCR) systems perform optimally on single-font monolingual texts, and have lower performance on bilingual and multilingual texts. For many OCR tasks it is necessary to accurately recognize characters from bilingual texts such as dictionaries or grammar books. We present a novel approach to segmenting bilingual text, easily extensible to more than two languag...

Optical character recognition (OCR) systems for machine-printed documents typically require large numbers of font styles and character models to work well. When given a document printed in an unseen font, the performance of those systems degrade even in the absence of noise. In this paper, we perform OCR in an unsupervised fashion without using any character models by using a cryptogram decodin...

Text detection and optical character recognition (OCR) in images of natural scenes is a fairly new computer vision area but yet very useful in numerous applicative areas. Although many implementations gain promising results, they are evaluated mostly on the private image collections that are very hard or even impossible to get. Therefore, it is very difficult to compare them objectively. Since ...

In this paper we present a simple yet effective approach to automatic OCR error detection and correction on a corpus of French clinical reports of variable OCR quality within the domain of foetopathology. While traditional OCR error detection and correction systems rely heavily on external information such as domain-specific lexicons, OCR process information or manually corrected training mater...

Language data for the Tesseract OCR system currently supports recognition of a number of languages written in Indic writing scripts. An initial study is described to create comparable data for Tesseract training and evaluation based on two approaches to character segmentation of Indic scripts; logical vs. visual. Results indicate further investigation of visual based character segmentation lang...