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Power Platform Community / Forums / Power Apps / problems with data det...
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problems with data detection with Invoice prebuilt model

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Posted on by 15

Hello, 

I'm currently  working on automating invoice porcessing using an Ai prebuilt model.

 

While it genrally works well with most invoices, i've noticed ocasional issues with detecting all items table properly.

 

I can understand the importance of the invoices quality or certain formats can be a challenge for the AI Models as it can confuse the prebuilt model, but it can correctly  recognize most of the items within the invoices, but occasionally it just skips some of them or the data detection is wrong.

 

Sometimes, reprocesing the invoice can fix the issue, but thats not the ideal solution.

 I've also tried to train a model, but i have the same result with some invoices.  

 

I would appreciate any insights or suggestions on how to improve the accuracy of the AI model for invoice processing.

the is an example of the data recognition issue.
the  rows in red are incorrect 

YanysM_0-1711554586482.png 

Description Quantity Unit price Amount

|AGUACATE KG2.1005.50 |11.55 |
|AJO TRENZA102.00|20.00 |
| ALBAHACA BANDEJAS101.25 |12.50 |
| APIO KILO102.90129.00 |
|CEBOLLINA MAZO13.50|3.50 |
LECHUGA GREEN LEAF KILO6|6.50|39.00|
ROMANA KILO6|LECHUGA6.00|36.00|
|LIMON AMARILLO UND100.75|7.50 |
|NARANJA IMP. UNIDAD50.91|4.55 |
|PAPA KG25.2003.15|79.38|
|REPOLLO VERDE KILO6.7003.05|20.44|
PAQ.2|RABANO3.75|7.50 |
| TOMATE 3 X 3 KILO254.70|117.50 |
| TOMATE CHERRY BANDEJA102.0020.00 |
|ZANAHORIA KILO102.35|23.50|

 

 

 

Thank you!

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I have the same question (0)
  • Antrod Profile Picture
    Moderator on at

    Hi @YanysM ,

     

    Thanks for bringing that to our attention.

     

    This seems to be caused by a misdetection of the words by the OCR service. It may be improved in the future as Microsoft is regularly improving its OCR capability but it can't be improved right now by AI Builder model training.

     

    If you are running a Power Automate flow to extract the information, I would recommend to include a cleaning step after data extraction to handle such case. For example, if you detect "|" or even letters in the quantity field, you could assume that a part of the string is not in the correct place. You could therefore add a logic that turns 

    ROMANA KILO 6|LECHUGA

    to

    LECHUGA ROMANA KILO 6

     

    Hope that could help!

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