![]() Captured business cards can be grouped by tags, so if you attend a conference (when that's possible again), you can make a tag for all the contacts you gained at the event. Business Card Reader saves you the hassle of carrying around dozens of little bits of paper and typing in contacts by hand, by recognizing contact data and saving contacts to Cardholder or your smartphone’s Contacts app. It’s available for iOS, Android and also has a web version to use on a PC. Do I really need this app now?īusiness Card Reader is a tool for scanning business cards and managing contacts. To make your work with business cards and contacts easier, learn more about ABBYY Business Card Reader (BCR) and what it can do. Even so, not much has changed – paper business cards are still widely used to exchange contact information. > - pattern matching: the regular expressions we use include wide tolerance for mistakes.In 2008, we launched the first Business Card Reader, which ran on the then-popular Symbian operating system. where the letter mistake is one listed in the ambigs files, with the correct spelling in the user dictionary) so we implemented our own dictionaries > - dictionaries: another big disappointment - from our testing we found that Tesseract applies the dictionary in less than 5% of the cases where it should (i.e. For these types of mistakes we go back to the source image to apply our own OCR of sorts. > - ambiguous letters such as i versus l: surprisingly, Tesseract makes a ton of incongruous mistakes that lead me to believe there is no feature analysis whatsoever - for example a 'y' may get mapped to 'g', even though there is 0% chance of that based on a wide open gap on top. For example VV is usually corrected back to W - but there are hundreds more cases > - obvious mistakes: this is by far the largest category of corrections we make. > - spacing: we don't trust any spacing determination by Tesseract and reevaluate every space indicated by Tesseract for possible elimination or consider every two letters for a possible space insertion > It's really a long list of approaches, including: > Von: [mailto: Im Auftrag von Patrick Questembert > I totally agree with Patrick, if you do the preprocessing well then I always get perfect result with tesseract, but I never tried ABBYY. > So in ABBYY you pay for the image preprocessing and in tesseract not. ![]() Under the "protection" of a regular expression for a specific pattern we have the flexibility to include hundreds of ambiguities (because these trigger only when they help complete a match which makes it more likely to be a valid substitution pattern matching: the regular expressions we use include wide tolerance for mistakes. dictionaries: another big disappointment - from our testing we found that Tesseract applies the dictionary in less than 5% of the cases where it should (i.e. ambiguous letters such as i versus l: surprisingly, Tesseract makes a ton of incongruous mistakes that lead me to believe there is no feature analysis whatsoever - for example a 'y' may get mapped to 'g', even though there is 0% chance of that based on a wide open gap on top. obvious mistakes: this is by far the largest category of corrections we make. spacing: we don't trust any spacing determination by Tesseract and reevaluate every space indicated by Tesseract for possible elimination or consider every two letters for a possible space insertion It's really a long list of approaches, including: IPhone / Android business card reader app. ABBY powers two iPhoneĪpps made by German company - Business Card Reader (by Shape Services)Īnd Card Reader (by xRoot Software) - and of course ABBY's own Installing the free version of ScanBizCards. You can also test instead on your Android or iPhone mobile device by when done testing please delete the test images from this demoĪccount (or get your own online account). click that image then "Image Editor" on top and OCR it If you tested on something *like* a business card (sparse text), not a Geared towards recognizing text on business cards so it would be best ScanBizCards is case #3 around Tesseract 3.01. However, if you compare Tesseract + image processing + heuristics &Ĭorrections, Tesseract actually beats ABBY hands down. Without applying any post-Tesseract heuristic, ABBY may have anģ. If you compare Tesseract and ABBY on a clean (processed) image, Processing is not an issue and refer to case 2 below.Ģ. Produced (for example) by a flatbed scanner, the lack of image Preprocessing to it, ABBY wins (because Tesseract's image processing If you compare Tesseract and ABBY on a same image, without applying
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