OCR for robots
Robotic tasks may involve reading and understanding written text. When conditions are optimal, camera mounted on robots allow them to interpret text without major obstacles: but oftentimes, this OCR task for robots needs to overcome difficulties, be these due to the position and type of the camera, lighting conditions, the quality of written characters, the shape of the object bearing the text or else. RSIP Vision engineers are experts also in this branch of OCR and can recommend the best solution for your project.
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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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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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Real time OCR in natural scenes
With the advancement in technology, the demand for OCR in natural environment is growing, even though outdoor conditions are far from being optimal for machine vision applications: occlusion of written text, text orientation, font style, blurring due to camera motion, and lighting conditions can prove themselves significant challenges in the task of performing real time OCR. Great progress has recently been made in the recognition of characters partially occluded and under heavy noise. RSIP Vision tells you how.
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Deep Learning For OCR
OCR used for the visual inspection of documents has found wide application in both industry and research, though it is more commonly found in connection with printed characters than for handwritten ones, mainly owing to the variability in handwritten characters’ shapes and styles. Hence the need for automatic recognition performed by vision-based tailor-made algorithms and adjustment. Deep Neural Networks as a learning mechanism to perform recognition have proved to be particularly powerful tools, due to their high accuracy in both spotting text region and deciphering the characters.
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