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Question 256 - MLS-C01 discussion

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A law firm handles thousands of contracts every day. Every contract must be signed. Currently, a lawyer manually checks all contracts for signatures.

The law firm is developing a machine learning (ML) solution to automate signature detection for each contract. The ML solution must also provide a confidence score for each contract page.

Which Amazon Textract API action can the law firm use to generate a confidence score for each page of each contract?

A.
Use the AnalyzeDocument API action. Set the FeatureTypes parameter to SIGNATURES. Return the confidence scores for each page.
Answers
A.
Use the AnalyzeDocument API action. Set the FeatureTypes parameter to SIGNATURES. Return the confidence scores for each page.
B.
Use the Prediction API call on the documents. Return the signatures and confidence scores for each page.
Answers
B.
Use the Prediction API call on the documents. Return the signatures and confidence scores for each page.
C.
Use the StartDocumentAnalysis API action to detect the signatures. Return the confidence scores for each page.
Answers
C.
Use the StartDocumentAnalysis API action to detect the signatures. Return the confidence scores for each page.
D.
Use the GetDocumentAnalysis API action to detect the signatures. Return the confidence scores for each page
Answers
D.
Use the GetDocumentAnalysis API action to detect the signatures. Return the confidence scores for each page
Suggested answer: A

Explanation:

The AnalyzeDocument API action is the best option to generate a confidence score for each page of each contract. This API action analyzes an input document for relationships between detected items. The input document can be an image file in JPEG or PNG format, or a PDF file. The output is a JSON structure that contains the extracted data from the document. The FeatureTypes parameter specifies the types of analysis to perform on the document. The available feature types are TABLES, FORMS, and SIGNATURES. By setting the FeatureTypes parameter to SIGNATURES, the API action will detect and extract information about signatures from the document. The output will include a list of SignatureDetection objects, each containing information about a detected signature, such as its location and confidence score. The confidence score is a value between 0 and 100 that indicates the probability that the detected signature is correct. The output will also include a list of Block objects, each representing a document page. Each Block object will have a Page attribute that contains the page number and a Confidence attribute that contains the confidence score for the page. The confidence score for the page is the average of the confidence scores of the blocks that are detected on the page. The law firm can use the AnalyzeDocument API action to generate a confidence score for each page of each contract by using the SIGNATURES feature type and returning the confidence scores from the SignatureDetection and Block objects.

The other options are not suitable for generating a confidence score for each page of each contract. The Prediction API call is not an Amazon Textract API action, but a generic term for making inference requests to a machine learning model. The StartDocumentAnalysis API action is used to start an asynchronous job to analyze a document. The output is a job identifier (JobId) that is used to get the results of the analysis with the GetDocumentAnalysis API action. The GetDocumentAnalysis API action is used to get the results of a document analysis started by the StartDocumentAnalysis API action. The output is a JSON structure that contains the extracted data from the document. However, both the StartDocumentAnalysis and the GetDocumentAnalysis API actions do not support the SIGNATURES feature type, and therefore cannot detect signatures or provide confidence scores for them.

References:

* AnalyzeDocument

* SignatureDetection

* Block

* Amazon Textract launches the ability to detect signatures on any document

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asked 16/09/2024
Sarah Pachowsky
33 questions
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