Enabling Digitalization with Data Capture and Optical Character Recognition (OCR) Technology 

OCR is a fundamental component of any data capture solution, helping businesses automate the scanning, data extraction and processing of documents, images and other files. This minimizes or even eliminates any need for manual data entry and maximizes the potential value of automation solutions.

OCR on its own, however, falls short in several ways. It is very sensitive to any distortion and can easily confuse similar characters, since it identifies them individually, without any context. For these reasons, typical OCR engines are not accurate enough for use in broader automation systems. Rather, it is far more powerful when coupled with computer vision technology into a complete "data capture" solution.

SoftWorks AI's Trapeze utilizes computer vision technology to provide many advantages:

Processes Hard-to-Read Images

Our Trapeze data capture engine uses a sophisticated voting engine, combining the results of several different data capture and recognition techniques. This system is tested and proven to identify difficult text often missed by competing OCR solutions. Computer vision also enables us to extract non-textual information from images, which OCR is not able to do.

More Efficient and Reliable

Trapeze data capture removes blank pages, and detects existing text layers within a document or image. This eliminates unnecessary recognition errors and vastly improves performance speed, enabling Trapeze users to achieve faster and more accurate data extraction and classification.

Advanced Pre-Processing Techniques

Softworks AI’s data capture engine analyzes and manipulates information in several ways that typical OCR engines cannot. This improves the quality of images before they are processed and makes our system even more efficient, versatile and precise.

Understands Information in Context

Our data capture software uses complex computer vision algorithms to analyze and understand each processed page on a much deeper level than competing solutions. Typical OCR engines simply scan for shapes and attempt to match them to known characters and words. With computer vision, however, we can consider every feature of an image or document, from color contrasts to co-linear structures, to more effectively understand each and every pixel. This vastly improves our accuracy, and allows us to properly understand tables, graphs, images and several other types of content that OCR does not pick up.