Using deep learning for invoice data extraction

This process can be broken into 3 steps:

  1. Digitize the invoices- Invoices are in the form of pdfs that need to be digitized. Depending on the quality of the input, we need to add an image preprocessing pipeline for best results.
  2. Extract data- Data extraction is done using AI algorithms. We can process this extracted information using Optical Character Recognition. Here, it is important to identify which piece of text corresponds to which field.
  3. Create database- After the data has been extracted, we need to create a database based on a unique identifier.

Benefits of Invoice Data Extraction

The concept behind invoice data extraction-Object Detection

  1. bx,by center of a bounding box
  2. bw width
  3. bh height
  4. C as probability corresponding to a class of an object (cat,dog,person etc.).
  1. img: define input image size
  2. batch: determine batch size
  3. epochs: define the number of training epochs.
  4. data: set the path to our yaml file
  5. cfg: specify our model configuration
  6. weights: specify a custom path to weights
  7. name: result names
  8. nosave: only save the final checkpoint
  9. cache: cache images for faster training
  1. source: input images directory or single image path or video path
  2. weights: trained model path
  3. conf: confidence threshold

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Software product development services company that builds world-class products & solutions by combining cutting-edge technologies for web, Cloud, data & devices

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Nitor Infotech Private Limited

Nitor Infotech Private Limited

Software product development services company that builds world-class products & solutions by combining cutting-edge technologies for web, Cloud, data & devices

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