• Neural-Inspired Algorithms
• Flexible OEM Integration

        Full Source Code

        API Library

        Plug-in Module

• Single-Pass Capture
• Supported Capture Devices

        Scanners, Cameras

        Mobile Devices

        Existing Image Files

HOW DOES PERFECTSCAN® WORK?

PerfectScan® has been developed based on the early visual path processing and recognition models found in the human visual system. Instead of processing an image at the pixel level, PerfectScan® takes it further and analyzes the entire image using a set of cognitive-based algorithms. These neural-inspired algorithms are tuned specifically to reduce noise, achieve perfect gamma correction, and obtain optimal image clarity. The end result is an output image that was never before thought possible. As shown in the figure below, PerfectScan® employs the following processes/techniques:

• Denoise. Both spatial and intensity information between a point and its neighboring points are considered, unlike the conventional linear filtering where only spatial information is considered. Edges and sharpness of the image are preserved.
• Symbol Extraction. Symbols or objects are analyzed and extracted from the image.
• Symbol Refinement. Disconnected symbols are intelligently repaired by connecting the missing line segments.
• Symbol Sharpening. During this phase, the edges of the symbols are sharpened to eliminate the noises near the edges, as well to reduce the pixilation effect.
• Background Removal. Background information is intensively dissected. The uniform background is dropped, whereas textured background is partially retained. In certain instances it is desirable to retain the background. Therefore, this functionality is optional.
• Global Threshold. Adaptive thresholding is employed. The optimal number of threshold levels is determined by computing the number of significant peaks from the image’s histogram.
• Cognitive Merging Process. This is the heart of PerfectScan®. All the intermediate results are combined and analyzed using a set of cognitive-based algorithms. Optimization goals reduce noise, achieve perfect gamma correction, and obtain optimal image clarity.