![OCR Check Scanner](https://dev.rsipvision.com/wp-content/uploads/2013/12/OCR-Checks1-300x155.jpg)
OCR Check Scanner
Due to the different styles of handwritten characters, digitalizing check information can be very challenging. But not enough for RSIP Vision’s engineers, who built software for a client to enable him to do just that: detect different handwriting as well as printed check information and transmit all this data from smartphones to the bank. The result is that bank customers using the app which we developed for our client can deposit checks by simply placing their smartphone over the check.
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![Pattern recognition lens markings](https://dev.rsipvision.com/wp-content/uploads/2017/02/Pattern-recognition-lens-markings.jpg)
Pattern Recognition for Lens Markings Detection
Lens marking refers to the placement of temporary and/or permanent marking, semi-visible laser engraving for the use of lens identification and trademarks, and accurate placement
![Visual Inspection of Semiconductors](https://dev.rsipvision.com/wp-content/uploads/2013/12/Visual-Inspection-Semiconductors1-300x215.jpg)
Visual Inspection of Semiconductors
Semiconductors mass production needs exceptional levels of precision. Among the many processes needed to insure quality and reliability of chips, we were asked to verify that silicon chip masks imprint the right information on silicon wafers. This is a complex, multi-process computer vision task, to solve which we produced an advanced algorithmic software and vision system, the key advantage of which resides in the capture of critical errors early on in the semiconductor production process.
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![](https://dev.rsipvision.com/wp-content/uploads/2017/01/OCR-Low-Contrast-Images.jpg)
OCR Software for Low Contrast Images
It is not uncommon that images needing automatic Optical Character Recognition were originally taken in low light or under direct sunlight. RSIP Vision has developed software which accurately binarizes vehicle number plates even in low contrast images, resulting in quick and accurate transmission of license plate details to the client’s database for further action.
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![Tear film formation and meibomian glands](https://dev.rsipvision.com/wp-content/uploads/2017/01/Tear-film-formation-and-meibomian-glands-300x240.jpg)
Meibomian gland dysfunction detection
Meibomian gland dysfunction is often seen as an early stage of dry eye syndrome. Indeed, Meibomian glands play a significant role in tears production by
![Eye with Glaucoma](https://dev.rsipvision.com/wp-content/uploads/2017/01/Glaucoma-300x181.jpg)
Glaucoma Detection
Glaucoma, a high intraocular pressure (IOP) pathology, leading to damage of the optic nerve, can be better detected using deep learning techniques. When it detects the optical disc (the visible section of the optic nerve), the deep learning algorithm helps assess glaucoma in an automated way, starting from the region of interest and providing a reliable probability for the disease, which the physician will use to support both diagnosis and treatment decisions.
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![Giant Retinal Tear](https://dev.rsipvision.com/wp-content/uploads/2017/01/Giant-Retinal-Tear-Retina-Detachment-300x236.jpg)
Retinal Detachment Detection
Retinal detachment occurs when part of the retina detaches itself from the pigmented cell layer of the RPE, depriving itself of blood and nutrition: this
![Ultrasound Imaging of Speckled Cartilage Tissue](https://dev.rsipvision.com/wp-content/uploads/2017/01/Cartilage-PP1-300x254.jpg)
Density Measurement of Damaged Cartilage
Interpretation of ultrasound images of cartilage is challenging since they display no obvious borders in the transition between tissues: the boundary between tissues can morph in both density and texture. Our software can process these problematic ultrasound images and automatically measure the density of cartilage in the knee. The main benefits of this procedure are its non-invasive nature and the efficient and accurate measurement of the cartilage it provides.
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![Coronary CT Angiography](https://dev.rsipvision.com/wp-content/uploads/2017/01/Coronary-CT-Angiography-230x300.jpg)
Coronary CT Angiography with Deep Learning
The production of 3D CT images of the heart requires a fast image processing technology, applied simultaneously on multiple scanned layers. To automatically separate the different components of the image, our software locates in the images the muscular layer (myocardium) of the heart needed for the rest of the segmentation in coronary CT angiography. Minimum graph cuts is the technique which provides the clearest tracking and the strongest segmentation results.
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![](https://dev.rsipvision.com/wp-content/uploads/2017/01/HIV-Orgenics-ImmunoComb-300x169.jpg)
Automated diagnostic reading of HIV
Human Immunodeficiency Virus is a double-strand RNA retrovirus, which translates into a DNA by reverse transcription in the cell cytoplasm and later integrated into the