The daily business of the financial industry involves a large amount of repetitive and inefficient input and verification work. Processing massive bill forms requires a significant amount of labor costs. Although there is currently traditional OCR technology, it has a series of problems such as being able to recognize bills, having few types of text, and slow recognition speed.

• Intelligent OCR inference servers need to have high inference performance
• While improving performance, enable model inference accuracy to meet application requirements

Financial software companies have launched various solutions such as license robots, bill robots, financial reporting robots, and AI training platforms based on various OCR algorithms such as bank document text recognition and financial document recognition using Nut Core Computing servers . Make OCR technology more efficient and intelligent. For example, the bill robot adopts the STR technology of the artificial intelligence deep learning engine, which supports automatic classification of multiple bills, can structurally recognize multiple fields, and output all text results by line. The entire process does not require any manual intervention, not only greatly improving work efficiency, but also reducing enterprise costs.