I note that the question indicates the use of an OCR scanner. Such devices have existed for decades and the dominant manufacturer of them back in the 199s was Caere Corporation. They were a two-part system consisting of a decoder box (which housed most of the electronics) and an input device. The most popular was a pistol-like wand although there were also slot- readers that would read from the edges of cheques and documents. An OCR scanner is limited to reading only a single line of . Because such devices were limited to scanning OCR characters and could do a number of preprocessing steps they were extremely accurate having a very low error-substitution rate. OCR characters are those printed in the fonts OCR-A s OCR-B s and E13B s . The latter was - and still does - exist on the bottom of printed cheques and can be read magnetically as well as optically. Unlike bar codes OCR characters are both human- and machine-readable. The preprocessing steps I mention could for example ensure that X number of characters had been seen by the reader thus ensuring that the operator hadn begun reading the line of characters partway through. Expected masks of characters could also be programmed into OCR reading systems so that a character string would only be successfully transmitted if it matched a specific pattern (e.g. two numbers followed by five alpha characters followed by five more numbers). The accuracy of any italic OCR scanning device or software is dependent greatly upon the font(s) it is reading their clarity and consistency and whether or not there is any further intelligence built into the system to check and determine con. In the English alphabet and number system characters many are alike-looking - consider the number 1 and the lower-case letter l or (zero) and upper-case O. This intelligence would determine and decode for example that the string Olliver isprised of all alphabetic characters because it amon name and not be inaccurately decoded as 11iver.
What, if any, is a good OCR scanner for an Android phone?
I highly rmended the FlashScan - PDF Scanner Scan Document - Apps on Google Play s Features of FlashScan App Document Scanner Crop scanning area Export picture from the gallery Apply Filter & Effects - Grayscale Magic color Black & White and Original Convert your saved Jpeg as PDF Or Jpeg format Rename Documents OCR Feature Scan QR Codes and Barcodes
Which is the best OCR (text scanner) app that works offline?
Hello You can use Accurascan a free OCR scanning app available for a trial version. This light-weight app is available for both Android and iOS devices. As it workspletely offline you can be assured of no virus or malware attack. Also the best thing about the app is that it scans in real-time at a terrific speed. However if you need an OCR scanning app to meet your business needs it is advisable to go for its SDK which is available for both mobile and web platforms. In short with the help of Accurascan's web SDK you can integrate Accurascan with any existing app or customize it effortlessly to meet your specific scanning needs. Hope this helps you. Thanks
Is it possible to scan a barcode with an OCR tech scanner from a web application?
Funny you should ask! Several years ago when I started working on OCR technologies a lot of customers would you ask this question the other way around - We know that we can scan barcodes and documents through OCR for web applications. But can this be done on mobile devices too? Short answer to your question is Yes. That where it all started. The process is fairly straightforward Build your web app that lets user upload an s to ZXing running locally or in the cloud and get the barcode content. Machine Learning Getting OCR out is a good start but from my experience in building OCR solutions this value in itself is not of much use. You need tobine this with other factors and weave it into an algorithm that can learn from your extraction results over time and use that to predict what the right is. ordered-list For business apps there are situations where the OCR engine is pretty confident of the extracted data but the does not add up in the con of all the other data around it. This is where classic OCR engines fails. A lot ofpanies have been able to get around this problem by building strong algorithms based on machine learning which can plug the gap in the OCR engine's readability.