Merge pull request #111159 from PatrickFarley/formre-updates

[cog serv] Formre updates

Author: ShannonLeavitt

articles/cognitive-services/form-recognizer/deploy-label-tool.md

@@ -7,7 +7,7 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: forms-recognizer
 ms.topic: how-to
-ms.date: 03/20/2020
+ms.date: 04/14/2020
 ms.author: pafarley
 ---
 
@@ -71,6 +71,7 @@ Follow these steps to create a new resource using the Azure portal:
    * Username (Optional) - Create a username. 
    * Password (Optional) - Create a secure password that you'll remember.
    * Image and tag - Set this to `mcr.microsoft.com/azure-cognitive-services/custom-form/labeltool:latest`
+   * Continuous Deployment - Set this to **On** if you want to receive automatic updates when the development team makes changes to the sample labeling tool.
    * Startup command - Set this to `./run.sh eula=accept`
 
    > [!div class="mx-imgBorder"]

articles/cognitive-services/form-recognizer/media/whats-new/formre-value-type.png

@@ -89,7 +89,7 @@ Explore the [REST API reference documentation](https://westus2.dev.cognitive.mic
 
 The input requirements for the receipt model are slightly different.
 
-* Format must be JPEG, PNG, BMP, PDF (text or scanned) or TIFF.
+* Format must be JPEG, PNG, PDF (text or scanned) or TIFF.
 * File size must be less than 20 MB.
 * Image dimensions must be between 50 x 50 pixels and 10000 x 10000 pixels.
 * PDF dimensions must be at most 17 x 17 inches, corresponding to Legal or A3 paper sizes and smaller.

articles/cognitive-services/form-recognizer/overview.md

@@ -8,7 +8,7 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: forms-recognizer
 ms.topic: quickstart
-ms.date: 02/19/2020
+ms.date: 04/14/2020
 ms.author: pafarley
 ---
 
@@ -100,7 +100,7 @@ Fill in the fields with the following values:
 In the sample labeling tool, projects store your configurations and settings. Create a new project and fill in the fields with the following values:
 
 * **Display Name** - the project display name
-* **Security Token** - Some project settings can include sensitive values, such as API keys or other shared secrets. Each project will generate a security token that can be used to encrypt/decrypt sensitive project settings. You can find security tokens in the Application Settings by clicking the gear icon in the lower corner of the left navigation bar.
+* **Security Token** - Some project settings can include sensitive values, such as API keys or other shared secrets. Each project will generate a security token that can be used to encrypt/decrypt sensitive project settings. You can find security tokens in the Application Settings by clicking the gear icon at the bottom of the left navigation bar.
 * **Source Connection** - The Azure Blob Storage connection you created in the previous step that you would like to use for this project.
 * **Folder Path** - Optional - If your source forms are located in a folder on the blob container, specify the folder name here
 * **Form Recognizer Service Uri** - Your Form Recognizer endpoint URL.
@@ -126,9 +126,9 @@ Click **Run OCR on all files** on the left pane to get the text layout informati
 Next, you'll create tags (labels) and apply them to the text elements that you want the model to recognize.
 
 1. First, use the tags editor pane to create the tags you'd like to identify.
-  1. Click **+** to create a new tag.
-  1. Enter the tag name.
-  1. Press Enter to save the tag.
+   1. Click **+** to create a new tag.
+   1. Enter the tag name.
+   1. Press Enter to save the tag.
 1. In the main editor, click and drag to select one or multiple words from the highlighted text elements.
 1. Click on the tag you want to apply, or press the corresponding keyboard key. The number keys are assigned as hotkeys for the first 10 tags. You can reorder your tags using the up and down arrow icons in the tag editor pane.
     > [!Tip]
@@ -140,15 +140,30 @@ Next, you'll create tags (labels) and apply them to the text elements that you w
     > * Don't include keys in your tagged fields—only the values.
     > * Table data should be detected automatically and will be available in the final output JSON file. However, if the model fails to detect all of your table data, you can manually tag these fields as well. Tag each cell in the table with a different label. If your forms have tables with varying numbers of rows, make sure you tag at least one form with the largest possible table.
 
+![Main editor window of sample labeling tool](../media/label-tool/main-editor.png)
 
-Follow the above steps to label five of your forms, and then move on to the next step.
+Follow the steps above to label at least five of your forms.
 
-![Main editor window of sample labeling tool](../media/label-tool/main-editor.png)
+### Specify tag value types
 
+Optionally, you can set the expected data type for each tag. Open the context menu to the right of a tag and select a type from the menu. This feature allows the detection algorithm to make certain assumptions that will improve the text-detection accuracy. It also ensures that the detected values will be returned in a standardized format in the final JSON output. 
+
+> [!div class="mx-imgBorder"]
+> ![Value type selection with sample labeling tool](../media/whats-new/formre-value-type.png)
+
+The following value types and variations are currently supported:
+* `string`
+    * default, `no-whitespaces`, `alphanumeric`
+* `number`
+    * default, `currency`
+* `date` 
+    * default, `dmy`, `mdy`, `ymd`
+* `time`
+* `integer`
 
 ## Train a custom model
 
-Click the Train icon (the train car) on the left pane to open the Training page. Then click the **Train** button to begin training the model. Once the training process completes, you'll see the following information:
+Click the Train icon on the left pane to open the Training page. Then click the **Train** button to begin training the model. Once the training process completes, you'll see the following information:
 
 * **Model ID** - The ID of the model that was created and trained. Each training call creates a new model with its own ID. Copy this string to a secure location; you'll need it if you want to do prediction calls through the REST API.
 * **Average Accuracy** - The model's average accuracy. You can improve model accuracy by labeling additional forms and training again to create a new model. We recommend starting by labeling five forms and adding more forms as needed.
@@ -163,7 +178,7 @@ After training finishes, examine the **Average Accuracy** value. If it's low, yo
 
 ## Analyze a form
 
-Click on the Predict (rectangles) icon on the left to test your model. Upload a form document that you haven't used in the training process. Then click the **Predict** button on the right to get key/value predictions for the form. The tool will apply tags in bounding boxes and will report the confidence of each tag.
+Click on the Predict (light bulb) icon on the left to test your model. Upload a form document that you haven't used in the training process. Then click the **Predict** button on the right to get key/value predictions for the form. The tool will apply tags in bounding boxes and will report the confidence of each tag.
 
 > [!TIP]
 > You can also run the Analyze API with a REST call. To learn how to do this, see [Train with labels using Python](./python-labeled-data.md).