| description |
|---|
Work with text in images, just like you work with encoded text. |
Visual text recognition 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.
Please 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).
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
{% tabs %} {% tab title="gRPC Python" %}
# Insert here the initialization code as outlined on this page:
# https://old-docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions
post_workflows_response = stub.PostWorkflows(
service_pb2.PostWorkflowsRequest(
user_app_id=userDataObject, # The userDataObject is created in the overview and is required when using a PAT
workflows=[
resources_pb2.Workflow(
id="visual-text-recognition-id",
nodes=[
resources_pb2.WorkflowNode(
id="detect-concept",
model=resources_pb2.Model(
id="2419e2eae04d04f820e5cf3aba42d0c7",
model_version=resources_pb2.ModelVersion(
id="75a5b92a0dec436a891b5ad224ac9170"
)
)
),
resources_pb2.WorkflowNode(
id="image-crop",
model=resources_pb2.Model(
id="ce3f5832af7a4e56ae310d696cbbefd8",
model_version=resources_pb2.ModelVersion(
id="a78efb13f7774433aa2fd4864f41f0e6"
)
),
node_inputs=[
resources_pb2.NodeInput(node_id="detect-concept")
]
),
resources_pb2.WorkflowNode(
id="image-to-text",
model=resources_pb2.Model(
id="9fe78b4150a52794f86f237770141b33",
model_version=resources_pb2.ModelVersion(
id="d94413e582f341f68884cac72dbd2c7b"
)
),
node_inputs=[
resources_pb2.NodeInput(node_id="image-crop")
]
),
]
)
]
),
metadata=metadata
)
if post_workflows_response.status.code != status_code_pb2.SUCCESS:
raise Exception("Post workflows failed, status: " + post_workflows_response.status.description){% endtab %}
{% tab title="gRPC Java" %}
import com.clarifai.grpc.api.*;
import com.clarifai.grpc.api.status.*;
// Insert here the initialization code as outlined on this page:
// https://old-docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions
MultiWorkflowResponse postWorkflowsResponse = stub.postWorkflows(
PostWorkflowsRequest.newBuilder()
.setUserAppId(UserAppIDSet.newBuilder().setAppId("{YOUR_APP_ID}"))
.addWorkflows(
Workflow.newBuilder()
.setId("visual-text-recognition-id")
.addNodes(
WorkflowNode.newBuilder()
.setId("detect-concept")
.setModel(
Model.newBuilder()
.setId("2419e2eae04d04f820e5cf3aba42d0c7")
.setModelVersion(
ModelVersion.newBuilder()
.setId("75a5b92a0dec436a891b5ad224ac9170")
)
)
)
.addNodes(
WorkflowNode.newBuilder()
.setId("image-crop")
.setModel(
Model.newBuilder()
.setId("ce3f5832af7a4e56ae310d696cbbefd8")
.setModelVersion(
ModelVersion.newBuilder()
.setId("a78efb13f7774433aa2fd4864f41f0e6")
)
)
.addNodeInputs(NodeInput.newBuilder().setNodeId("detect-concept"))
)
.addNodes(
WorkflowNode.newBuilder()
.setId("image-to-text")
.setModel(
Model.newBuilder()
.setId("9fe78b4150a52794f86f237770141b33")
.setModelVersion(
ModelVersion.newBuilder()
.setId("d94413e582f341f68884cac72dbd2c7b")
)
)
.addNodeInputs(NodeInput.newBuilder().setNodeId("image-crop"))
)
)
.build()
);
if (postWorkflowsResponse.getStatus().getCode() != StatusCode.SUCCESS) {
throw new RuntimeException("Post workflows failed, status: " + postWorkflowsResponse.getStatus());
}{% endtab %}
{% tab title="gRPC NodeJS" %}
// Insert here the initialization code as outlined on this page:
// https://old-docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions
stub.PostWorkflows(
{
user_app_id: {
app_id: "e83440590d104cee97ef84af1856837d"
},
workflows: [
{
id: "visual-text-recognition-id",
nodes: [
{
id: "detect-concept",
model: {
id: "2419e2eae04d04f820e5cf3aba42d0c7",
model_version: {
id: "75a5b92a0dec436a891b5ad224ac9170"
}
}
},
{
id: "image-crop",
model: {
id: "ce3f5832af7a4e56ae310d696cbbefd8",
model_version: {
id: "a78efb13f7774433aa2fd4864f41f0e6"
}
},
node_inputs: [
{node_id: "detect-concept"}
]
},
{
id: "image-to-text",
model: {
id: "9fe78b4150a52794f86f237770141b33",
model_version: {
id: "d94413e582f341f68884cac72dbd2c7b"
}
},
node_inputs: [
{node_id: "image-crop"}
]
},
]
}
]
},
metadata,
(err, response) => {
if (err) {
throw new Error(err);
}
if (response.status.code !== 10000) {
console.log(response.status);
throw new Error("Post workflows failed, status: " + response.status.description);
}
}
);{% endtab %}
{% tab title="cURL" %}
curl -X POST 'https://api.clarifai.com/v2/users/me/apps/{{app}}/workflows' \
-H 'Authorization: Key {{PAT}}' \
-H 'Content-Type: application/json' \
--data-raw '{
"workflows": [
{
"id": "visual-text-recognition-id",
"nodes": [
{
"id": "detect-concept",
"model": {
"id": "2419e2eae04d04f820e5cf3aba42d0c7",
"model_version": {
"id": "75a5b92a0dec436a891b5ad224ac9170"
}
}
},
{
"id": "image-crop",
"model": {
"id": "ce3f5832af7a4e56ae310d696cbbefd8",
"model_version": {
"id": "a78efb13f7774433aa2fd4864f41f0e6"
}
},
"node_inputs": [
{
"node_id": "general-concept"
}
]
},
{
"id": "image-to-text",
"model": {
"id": "9fe78b4150a52794f86f237770141b33",
"model_version": {
"id": "d94413e582f341f68884cac72dbd2c7b"
}
},
"node_inputs": [
{
"node_id": "image-crop"
}
]
},
]
}
]
}'
{% endtab %} {% endtabs %}