We have a multi-layered framework to harness the combined power of machine learning algorithms, OCR, and a series of validation processes to make invoice and expense handling more efficient.

Our Framework

The OCR technology converts the images to text. This is an important step as this will define the quality of data that is fed into the following steps. We use a combination of in-house and best-in class OCR technology to get the best results.
Natural Language Processing
Statistical models will classify and extract the relevant data from receipts.
Business Validation
The validation process will run deterministic rule sets on the extracted data to increase relevance and accuracy.
Exception Handling
Exception that arise are corrected and the corresponding data is fed into the iterative learning process.
Scanned documents and images of invoices and receipts received in multiple formats are converted to text via OCR. We use best-in-class OCR technologies to get the best results.
Natural Language Processing (NLP) will classify and extract the relevant data from invoices and receipts. With NLP, we focus on the content of the documents rather than the format. 
Business validation adds an extra layer of checks to ensure data accuracy. An example of business validation would be checking if the summation of line items corresponds to the total amount.These validations apply deterministic rules on the outcomes of stochastic models, ensuring data consistency.
If an exception is detected, invoices or receipts will be reviewed by an operator. This ensures that you get accurate data. As part of the iterative learning process, the corrected data is also used to train the NLP models to reduce exceptions in the future.

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