Optical Character Recognition (OCR)

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:

Voting Groups (horizontal)@2xProcesses 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.

Context-Aware Recognition

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. 


Leverage Document Imaging Expertise to Maximize OCR Accuracy

With years of research and market experience, the SoftWorks AI team has developed numerous ways to improve the quality of documents before processing to maximize OCR accuracy.

Process Scanned Images, Digitally Born Documents, and Complex Hybrids

With our ability to identify and bypass recognition for existing text layers, Trapeze enables efficient processing of very complex batches of documents from various input sources.

Increase Touchless Automation and Take a Step Toward Digital Transformation

Higher OCR accuracy means higher quality data extraction and classification. It also reduces the need for manual intervention in the process, greatly increasing speed and reducing associated labor costs for an extremely efficient workflow.