Hi, at the moment, labels can be string / date / number. Could be better to differentiate string types like person / address / ... This is possible in TextAnalyze service.
To improve our training, do Form Recognizer use these features to extract labels:
- NLP ? (words type adjective / verbs, ...)
- words before / after the label ?
- word case ? (1st capital letter / all upper-case / ...)
- text position in document ? (top right / center / ...)
Thanks for info1 vote
In the Azure form recognizer official website "https://azure.microsoft.com/en-in/services/cognitive-services/form-recognizer/" few examples have tables in the sample file. In output also we have an attribute called table in sample json. Please guide us in labeling a table in a custom layout form.15 votes
Form Recognizer discovers and extracts tables automatically. Table results are part of the pageResults section in the JSON output. If the table in the form was not discovered you can label tables a values by labeling each table cell and training with the maximum number of rows in the tables. Form Recognizer does not yet support labeling tables as tables.
The attached example is used for training, but the trained model is not able to segregate keys and values. All the keys are set-up to "Tokens" value ( eg: "0": [ "Tokens" ])
Analyze Forms component will only extract some of the data, and will anyway allocate all of them on only one key.
Is there a version of Form Recognizer that works better with Japanese characters ? I am currently using the one on West Europe.1 vote
Forms usually have complex structures as well and that is where automated services are needed. I have forms that contain merged cells, and nested tables. On documentation of form-recognizer, it is stated that such forms are not supported. Does Microsoft looking forward towards making form-recognizer capable for such forms as well? If yes, any expected time frame?13 votes
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