jhubar / invoiceprocessing

Invoice processing

Library documentation

• transformer: layoutLM

State of the art documentation

• Gradient-Based Learning Applied to document Recognition
• ResNetImplementation
• Fine-Tuning Transformer Model for Invoice Recognition
• LayoutLM: Pre-training of Text and Layout for Document Image Understanding

How do you measure the performance the goodnest of our prediciton

A standard performance indicator for object detection is to evaluate the intersection over union (IoU) between a predicted bounding box $\hat{B}$ and an annotated bounding box $B$,

 = \frac{\text{area}(B \cap \hat{B})}{\text{area}(B \cup \hat{B})})

-(Gradient-Based Learning Applied to document Recognition)[https://github.com/jhubar/invoiceProcessing/blob/master/Documentation/lecun-98.pdf]