Optical Mark Recognition (OMR) is an important part of forms recognition but can be surprisingly complex. On the surface, it looks easy to detect whether a box is filled or unfilled by just counting the pixels. While that may be true for the small area, how do you handle an image with hundreds of checkboxes like a multiple-choice exam sheet or survey?

Enterprise Content Management (ECM) and eDiscovery aren’t necessarily all that new, but it’s becoming increasingly important to implement due to the continuing transition into a digital age. Businesses, especially large corporations, have a massive amount of electronically stored information (ESI) that they would be wise to retain for future possible legal ramifications, so the stakes are high.

Introduction

A wide variety of businesses are embracing technology as a means to enhance the way customers provide their information on the front-end of the customer service process. Upon arrival at the doctor's office, car dealership, gym or even a restaurant, customers might be handed a tablet or directed to a kiosk to check in. This is an exciting change for device manufacturers and software developers, but does it always translate to better customer service?

If the process is difficult or takes too long, customers are likely to get annoyed, or worse, take their business elsewhere. Therefore using technology for technology's sake isn't enough. To get the most out of their investment, business owners can use various imaging technologies to capture customer information to speed up the process and improve customer satisfaction. An ideal solution would capture information from a document or form of ID customers already have on them such as a driver's license. This white paper will explore how programmers can use LEADTOOLS Driver's License Recognition or Barcode Recognition to extract identifying information from driver's licenses.

Few things have as great an impact on document imaging as cleanup. Its benefits reach far beyond the the improved visuals and readability. Document image cleanup is the salt to the meatier document imaging technologies like OCR, barcode, PDF, forms recognition, archival, and the like—it enhances them.

• Accuracy - Images are rarely perfect. Colors, angles, imperfections in the original document all have an affect on the accuracy of recognition technologies. By correctly aligning the image and removing obstacles around the important areas, the recognition processes can scan the image and look for the predictable patterns that make up the text and data that you wish to extract.
• Compression