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Visual Text Recognition

Work with text in images, just like you work with encoded text

Visual text recognition (VTR) helps you convert printed text in images and videos into machine-encoded text. You can input a scanned document, a photo of a document, a scene-photo (such as the text on signs and billboards), or text superimposed on an image (such as in a meme), and output the words and individual characters present in the images.

VTR lets you "digitize" text so that it can be edited, searched, stored, displayed, and analyzed.

Note

The current version of our VTR model is not designed for use with handwritten text or documents with tightly-packed text—like you might see on the page of a novel, for example. ::::

How VTR Works​

VTR works by first detecting the location of text in your photos or video frames, then cropping the region where the text is present, and then finally running a specialized classification model that will extract text from the cropped image. To accomplish these different tasks, you will need to configure a workflow.

You will then add these three models to your workflow:

  • Visual Text Detection
  • 1.0 Cropper
  • Visual Text Recognition

The initialization code used in the following example is outlined in detail on the client installation page. :::

Building a VTR Workflow​

###################################################################################
# In this section, we set the user authentication, app ID, and the details of the 
# VTR Workflow we want to build. Change these strings to run your own example.
##################################################################################

USER_ID = 'YOUR_USER_ID_HERE'
# Your PAT (Personal Access Token) can be found in the Account's Security section
PAT = 'YOUR_PAT_HERE'
APP_ID = 'YOUR_APP_ID_HERE'
# Change these to build your own VTR Workflow
WORKFLOW_ID = 'visual-text-recognition-id'

WORKFLOWNODE_ID_1 = 'detect-concept'
MODEL_ID_1 = '2419e2eae04d04f820e5cf3aba42d0c7'
MODEL_VERSION_ID_1 = '75a5b92a0dec436a891b5ad224ac9170'

WORKFLOWNODE_ID_2 = 'image-crop'
MODEL_ID_2 = 'ce3f5832af7a4e56ae310d696cbbefd8'
MODEL_VERSION_ID_2 = 'a78efb13f7774433aa2fd4864f41f0e6'

WORKFLOWNODE_ID_3 = 'image-to-text'
MODEL_ID_3 = '9fe78b4150a52794f86f237770141b33'
MODEL_VERSION_ID_3 = 'd94413e582f341f68884cac72dbd2c7b'

##########################################################################
# YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
##########################################################################

from clarifai_grpc.channel.clarifai_channel import ClarifaiChannel
from clarifai_grpc.grpc.api import resources_pb2, service_pb2, service_pb2_grpc
from clarifai_grpc.grpc.api.status import status_code_pb2

channel = ClarifaiChannel.get_grpc_channel()
stub = service_pb2_grpc.V2Stub(channel)

metadata = (('authorization', 'Key ' + PAT),)

userDataObject = resources_pb2.UserAppIDSet(user_id=USER_ID, app_id=APP_ID) # The userDataObject is required when using a PAT

post_workflows_response = stub.PostWorkflows(
    service_pb2.PostWorkflowsRequest(
        user_app_id=userDataObject,  
        workflows=[
            resources_pb2.Workflow(
                id=WORKFLOW_ID,
                nodes=[
                    resources_pb2.WorkflowNode(
                        id=WORKFLOWNODE_ID_1,
                        model=resources_pb2.Model(
                            id=MODEL_ID_1,
                            model_version=resources_pb2.ModelVersion(
                                id=MODEL_VERSION_ID_1
                            )
                        )
                    ),
                    resources_pb2.WorkflowNode(
                        id=WORKFLOWNODE_ID_2,
                        model=resources_pb2.Model(
                            id=MODEL_ID_2,
                            model_version=resources_pb2.ModelVersion(
                                id=MODEL_VERSION_ID_2
                                )
                            ),
                            node_inputs=[
                                resources_pb2.NodeInput(node_id=WORKFLOWNODE_ID_1)
                            ]
                        ),
                    resources_pb2.WorkflowNode(
                        id=WORKFLOWNODE_ID_3,
                        model=resources_pb2.Model(
                            id=MODEL_ID_3,
                            model_version=resources_pb2.ModelVersion(
                                id=MODEL_VERSION_ID_3
                                )
                            ),
                            node_inputs=[
                                resources_pb2.NodeInput(node_id=WORKFLOWNODE_ID_2)
                            ]
                        ),
                ]
            )
        ]
    ),
    metadata=metadata
)

if post_workflows_response.status.code != status_code_pb2.SUCCESS:
    raise Exception("Post workflows failed, status: " + post_workflows_response.status.description)