OCR

Usage notes

Procedure

Step 1: Create a dataset

1. Go to the Computer Vision Model Training page.
   1. Log on to the PAI console.
   2. In the left-side navigation pane, choose AI Industry Plug-In > Visual Modeling Platform Plug-in.
2. On the Computer Vision Model Training page, find the instance that you want to manage and click Open in the Operation column.
3. In the Data Preparation step, click New Dataset.
4. In the New Dataset panel, set the parameters.

   Parameter	Description
   Dataset name	The name of the dataset. The name must be 1 to 30 characters in length and can contain underscores (_) and hyphens (-). It must start with a letter or digit.
   Description	The description of the dataset. The description helps distinguish different datasets.
   Storage type	Only OSS is supported. You cannot change the value.
   OSS path	The OSS path where the images for model training are stored.

5. Click Confirm.
   The visual modeling platform plug-in automatically creates indexes on images and labeling data, but does not save the indexed images. The plug-in can retrieve your images in OSS to train models only after the plug-in is authorized. You can view the information of datasets in the Dataset list section. If the status of the dataset changes from Data import to To be manually marked or Labeling completed, the dataset is created.

Step 2: Label images

If your dataset contains unlabeled images, you can label them on the AutoLearning platform.

1. In the Dataset list section of the Data Preparation step, click Labeling in the Operation column.
2. On the Labeling tab, label all images and click Submit.
3. Click the Preview tab to view the labeling results.

Step 3: Create a task

1. In the Data Preparation step, click the Training tasks step in the upper part of the page.
2. In the Training tasks step, click New task.
3. In the New task panel, set the parameters.

   Step	Parameter	Description
   Basic Information	Task name	The name of the task. The name must be 1 to 30 characters in length and can contain underscores (_) and hyphens (-). It must start with a letter or digit.
   	Description	The description of the task. The description helps distinguish different tasks.
   Dataset	Select dataset	Select the created dataset as the training dataset.
   Algorithm and training	Select algorithm	OCR (High Performance): balances the inference performance of the server in the cloud and clients and provides fast prediction services.
   	Resource configuration	Set the Number of GPUs and GPU type parameters for the training task.
   	Show Advanced Settings	Click Show Advanced Settings to customize the algorithm parameters that are involved in model training. For more information, see Table 1. If you customize no parameters in the Show Advanced Settings section, default values are used.

   Table 1. Parameters in the Show Advanced Settings section

   Parameter	Description	Default value
   Data Enhancement	The following data enhancement methods are supported: Rotate: Rotate an image. Blur: Blur an image. Noise: Add noises to an image. Shear: Perform a shearing on an image. FlipLR: Flip an image left and right. FlipUD: Flip an image up and down.	Rotate and Noise
   Model width	Valid values: 0.35, 0.5, 0.75 and 1.	0.75
   Epoch Training	The number of epochs for model training.	100
   Optimizer	The optimization algorithms for model training. Valid values: Adam RmsProp Momentum	Adam
   Initialize learning rate	The initial learning rate during model training.	0.001
   Quantization compression	Specifies whether to perform quantization compression.	Yes

4. Click Start training.

Step 4: View training details

1. In the Training tasks step, click Training details in the Operation column of the task that you created.
2. On the page that appears, you can perform the following operations.

   Operation	Description
   View the training progress	On the Training process tab, view the training progress and relevant information in the Basic information section.
   Terminate the training task	On the Training process tab, click Terminate task.
   View the node information	On the Training process tab, click a node. In the Node Information panel, view the status of the node and the information in the Basic information and Step information sections.
   View training logs	On the Training process tab, click a node. In the Node Information panel, click the Log tab.

Step 5: Generate a mini program to test the model

1. After the training is complete, click Model and deploy in the upper-right corner of the training details page.
2. In the Model and deploy step, scan the QR code by using the Alipay app.
   The values of the following model metrics are calculated based on a validation set. A validation set is a portion of the training data. By default, 10% of the training data is extracted and used as a validation set.

   • loss: calculates the loss between ground truth and the predicted value by using the loss function. A lower loss indicates a more precise model.
   • model_size: obtains the model size based on optimization methods such as training, quantization, and encoding.
3. Use the mini program to scan objects to test how the model recognizes characters in real time.

Step 6: Deploy the model

1. In the Model and deploy step, click Go to PAI-EAS deployment.
2. Set model parameters.
   1. In the Model Configuration panel, set the Custom Model Name and Resources Type parameters. Other parameters are automatically set.
   2. Click Next.
3. In the Deployment details and confirmation panel, set the parameters.

   Parameter	Description
   Number Of Instances	Click the or icon to adjust the number of instances.
   Quota	The specifications of an instance. This parameter is displayed only when the Resources Type parameter is set to CPU. One quota contains one core and 4 GB of memory.
   Resources on a single instance	The specifications of a GPU server. This parameter is displayed only when the Resources Type is set to GPU.

   .
4. Click Deploy.
   The Elastic Algorithm Service page appears. If the status of the model changes to Running in the State column, the model is deployed.
5. Call the model service.
   Make an API call

   • HTTP method:

     POST.

   • Request URL:
     After the model is deployed as a service on the server, a public endpoint is automatically generated. To view the values of the Access address and Token parameters, perform the following steps:

     1. On the Elastic Algorithm Service page, click Invoke Intro in the Service Method column of the service that you want to call.
     2. In the Invoke Intro dialog box, click the Public Network Invoke tab to view the values of the Access address and Token parameters.
   • Request body

     {
       "dataArray":[
         {
           "name":"image",
           "type":"stream",
           "body":"Base64-encoded data"
         }
       ]
     }

     Parameter	Required	Type	Description
     name	No	STRING	N/A
     type	No	STRING	The type of the data. The default type is stream and cannot be changed.
     body	Yes	STRING	The data of an image. The data must be Base64-encoded. Images in JPG, PNG, and BMP formats are supported.

   • Response parameters

     Parameter	Type	Description
     success	BOOL	Indicates whether the call is successful.
     result	OBJECT	The return result.
     output	ARRAY	The recognition result that was returned in an array.
     label	STRING	The label of the image. The label represents the category of the image.
     conf	NUMBER	The confidence level.
     pos	ARRAY	The relative coordinates (x,y) of a detection frame. The relative coordinates are stored in the order of upper left, upper right, lower right, and lower left.
     meta	OBJECT	The information about the image.
     height	NUMBER	The height of the image.
     width	NUMBER	The width of the image.

   • Error codes

     Error code	Error message	Description
     1001	INPUT_FORMAT_ERROR	The error message returned because the input format is invalid. For example, a required parameter is missing. Check whether the input format is valid.
     1002	IMAGE_DECODE_ERROR	The error message returned because the image failed to be decoded. The image is not in a supported format, such as JPG and PNG. Check the image format.
     2001	UNKNOWN_ERROR	The error message returned because an internal server error occurred.
     2002	GET_INSTANCE_ERROR	The error message returned because the system failed to find the instance. This error may occur due to insufficient resources. Increase resources such as the number of CPU cores and memory size.
     2003	MODEL_FORWARD_ERROR	The error message returned because a model inference failure occurred. This error is caused by an internal server error.

   Examples

   • Sample request

     curl http://****.cn-shanghai.pai-eas.aliyuncs.com/api/predict/**** -H 'Authorization:****==' -d '{"dataArray": [{"body": "****", "type": "stream", "name": "image"}]}'

     Replace the URL, token, and Base64-encoded information in this example with actual values.
   • Sample response

     {
       "success":true,
       "result":{
         "output":[
           {
             "type":"cv_common",
             "body":[
               {
                 "label":"06174727742034996",
                 "conf":0.95,
                 "pos":[[468,992],[976,1012],[972,1072],[464,1052]]
               }
             ]
           }
         ],
         "meta":{
           "height":1920,
           "width":1080
         }
       }
     }

   • Sample error response
     If a request error occurs, the response contains the following parameters:

     • errorCode: the error code.
     • errorMsg: the error message.

     For example, if the request does not contain the dataArray field, the following response is returned:

     {
       "success":false,
       "errorCode":"1001",
       "errorMsg":"INPUT_FORMAT_ERROR"
     }