PDF Labels and Classifications

PDFs support annotation using bounding box, polygon, and bitmask object labels and classifications. Using the Encord SDK you can import labels and classifications directly to PDFs that already exist in an Annotate Project. You can also use the SDK to view labels and classifications that exist on a PDF.

Bounding Boxes and PDFs

The following screenshot provides the Ontology used with the code example.

Bounding Boxes and PDFs
# Import dependencies
from encord import EncordUserClient, Project
from encord.objects import ChecklistAttribute, Object, ObjectInstance, Option, RadioAttribute, TextAttribute
from encord.objects.coordinates import BoundingBoxCoordinates

# SSH and Project details
SSH_PATH = "/Users/chris-encord/ssh-private-key.txt"
PROJECT_ID = "12f1ebfb-bfdc-4682-a82b-bc20e5e01416"

# Create user client
user_client: EncordUserClient = EncordUserClient.create_with_ssh_private_key(
    ssh_private_key_path=SSH_PATH,
    # For US platform users use "https://api.us.encord.com"
    domain="https://api.encord.com",
)

# Get project
project: Project = user_client.get_project(PROJECT_ID)
ontology_structure = project.ontology_structure

# Get ontology object
box_ontology_object: Object = ontology_structure.get_child_by_title(title="PDF BB", type_=Object)

# Define radio attribute for correction types
correction_radio_attribute = ontology_structure.get_child_by_title(type_=RadioAttribute, title="Corrections BB")
english_correction_option = correction_radio_attribute.get_child_by_title(type_=Option, title="English corrections")
chinese_correction_option = correction_radio_attribute.get_child_by_title(type_=Option, title="繁體中文修正")

# Define checklist attributes
english_checklist_attribute = ontology_structure.get_child_by_title(type_=ChecklistAttribute, title="English")
en_ca_option = english_checklist_attribute.get_child_by_title(type_=Option, title="en-ca")
en_gb_option = english_checklist_attribute.get_child_by_title(type_=Option, title="en-gb")
en_us_option = english_checklist_attribute.get_child_by_title(type_=Option, title="en-us")

chinese_checklist_attribute = ontology_structure.get_child_by_title(type_=ChecklistAttribute, title="繁體中文")
zh_tw_option = chinese_checklist_attribute.get_child_by_title(type_=Option, title="zh-tw")
zh_hk_option = chinese_checklist_attribute.get_child_by_title(type_=Option, title="zh-hk")

# Define text attributes
english_correction_text_attribute = ontology_structure.get_child_by_title(type_=TextAttribute, title="Correction text")
chinese_correction_text_attribute = ontology_structure.get_child_by_title(type_=TextAttribute, title="更正")

# Example label data (you can adjust this)
pdf_labels = {
    "the-iliad.pdf": {
        # Specify the page number in the PDF. In the example below, page number 103 is labeled
        103: {
            "label_ref": "pdf_label_001",
            "coordinates": BoundingBoxCoordinates(height=0.4, width=0.4, top_left_x=0.1, top_left_y=0.1),
            "correction_type": "English corrections",
            "checklist_options": "en-ca, en-gb",
            "text_correction": "Fixed typo in English text.",
        }
    },
    "dracula.pdf": {
        # Specify the page number in the PDF. In the example below, page number 17 is labeled
        17: {
            "label_ref": "pdf_label_002",
            "coordinates": BoundingBoxCoordinates(height=0.3, width=0.5, top_left_x=0.2, top_left_y=0.2),
            "correction_type": "繁體中文修正",
            "checklist_options": "zh-tw",
            "text_correction": "修正了中文繁體的標點符號。",
        }
    }
}

# Loop through each data unit (image, video, etc.)
for data_unit, frame_coordinates in pdf_labels.items():
    object_instances_by_label_ref = {}

    # Get the label row for the current data unit
    label_row = project.list_label_rows_v2(data_title_eq=data_unit)[0]
    label_row.initialise_labels()

    # Loop through the frames for the current data unit
    for frame_number, items in frame_coordinates.items():
        if not isinstance(items, list):  # Single or multiple objects in the frame
            items = [items]

        for item in items:
            label_ref = item["label_ref"]
            coord = item["coordinates"]
            correction_type = item["correction_type"]
            checklist_options_str = item.get("checklist_options", "")
            text_correction = item.get("text_correction", "")

            # Check if label_ref already exists for reusability
            if label_ref not in object_instances_by_label_ref:
                box_object_instance: ObjectInstance = box_ontology_object.create_instance()
                object_instances_by_label_ref[label_ref] = box_object_instance  # Store for reuse

                # Set correction type (radio attribute)
                if correction_type == "English corrections":
                    box_object_instance.set_answer(attribute=correction_radio_attribute, answer=english_correction_option)

                    # Set checklist options for English
                    checklist_answers = []
                    for option in [opt.strip() for opt in checklist_options_str.split(",")]:
                        if option == "en-ca":
                            checklist_answers.append(en_ca_option)
                        elif option == "en-gb":
                            checklist_answers.append(en_gb_option)
                        elif option == "en-us":
                            checklist_answers.append(en_us_option)

                    if checklist_answers:
                        box_object_instance.set_answer(
                            attribute=english_checklist_attribute,
                            answer=checklist_answers,
                            overwrite=True,
                        )

                    # Set text correction
                    if text_correction:
                        box_object_instance.set_answer(attribute=english_correction_text_attribute, answer=text_correction)

                elif correction_type == "繁體中文修正":
                    box_object_instance.set_answer(attribute=correction_radio_attribute, answer=chinese_correction_option)

                    # Set checklist options for Chinese
                    checklist_answers = []
                    for option in [opt.strip() for opt in checklist_options_str.split(",")]:
                        if option == "zh-tw":
                            checklist_answers.append(zh_tw_option)
                        elif option == "zh-hk":
                            checklist_answers.append(zh_hk_option)

                    if checklist_answers:
                        box_object_instance.set_answer(
                            attribute=chinese_checklist_attribute,
                            answer=checklist_answers,
                            overwrite=True,
                        )

                    # Set text correction
                    if text_correction:
                        box_object_instance.set_answer(attribute=chinese_correction_text_attribute, answer=text_correction)

            else:
                # Reuse existing instance across frames
                box_object_instance = object_instances_by_label_ref[label_ref]

            # Assign the object to the frame and track it
            box_object_instance.set_for_frames(coordinates=coord, frames=frame_number)

    # Add object instances to label_row **only if they have frames assigned**
    for box_object_instance in object_instances_by_label_ref.values():
        if box_object_instance.get_annotation_frames():  # Ensures it has at least one frame
            label_row.add_object_instance(box_object_instance)

    # Upload all labels for this data unit (video/image) to the server
    label_row.save()

print("Labels with English and Mandarin corrections have been added for all data units.")

Polygons and PDFs

Polygons offer a number of options when labeling PDFs.

Polygons can have simple and complex shapes, including being enclosed in one another, and encompassing separate regions. In each case the polygon’s coordinates are arranged in a different way.

To specify coordinates for polygons use the following format:

PolygonCoordinates(polygons=[[[PointCoordinate(x1, y1), PointCoordinate(x2, y2),...]]]

For more information on polygon labels go here

This example uses multiple polygons to label the PDF.

The following screenshot provides the Ontology used with the code example.

Polygons and PDFs
# Import dependencies
from encord import EncordUserClient, Project
from encord.objects import ChecklistAttribute, Object, ObjectInstance, Option, RadioAttribute, TextAttribute
from encord.objects.coordinates import PolygonCoordinates, PointCoordinate

# SSH and Project details
SSH_PATH = "/Users/chris-encord/ssh-private-key.txt"
PROJECT_ID = "12f1ebfb-bfdc-4682-a82b-bc20e5e01416"

# Create user client
user_client: EncordUserClient = EncordUserClient.create_with_ssh_private_key(
    ssh_private_key_path=SSH_PATH,
    # For US platform users use "https://api.us.encord.com"
    domain="https://api.encord.com",
)

# Get Project
project: Project = user_client.get_project(PROJECT_ID)
ontology_structure = project.ontology_structure

# Get ontology object
polygon_ontology_object: Object = ontology_structure.get_child_by_title(title="PDF PG", type_=Object)

# Define radio attribute for correction types
correction_radio_attribute = ontology_structure.get_child_by_title(type_=RadioAttribute, title="Corrections PG")
english_correction_option = correction_radio_attribute.get_child_by_title(type_=Option, title="English corrections")
chinese_correction_option = correction_radio_attribute.get_child_by_title(type_=Option, title="繁體中文修正")

# Define checklist attributes
english_checklist_attribute = ontology_structure.get_child_by_title(type_=ChecklistAttribute, title="English")
en_ca_option = english_checklist_attribute.get_child_by_title(type_=Option, title="en-ca")
en_gb_option = english_checklist_attribute.get_child_by_title(type_=Option, title="en-gb")
en_us_option = english_checklist_attribute.get_child_by_title(type_=Option, title="en-us")

chinese_checklist_attribute = ontology_structure.get_child_by_title(type_=ChecklistAttribute, title="繁體中文")
zh_tw_option = chinese_checklist_attribute.get_child_by_title(type_=Option, title="zh-tw")
zh_hk_option = chinese_checklist_attribute.get_child_by_title(type_=Option, title="zh-hk")

# Define text attributes
english_correction_text_attribute = ontology_structure.get_child_by_title(type_=TextAttribute, title="Correction text")
chinese_correction_text_attribute = ontology_structure.get_child_by_title(type_=TextAttribute, title="更正")

# Example label data (you can adjust this)
pdf_labels = {
    "the-iliad.pdf": {
        # Specify the page number in the PDF. In the example below, page number 103 is labeled
        103: {
            "label_ref": "pdf_label_001",
            "coordinates": PolygonCoordinates(polygons=[
                [
                    # First rectangle
                    [
                        PointCoordinate(0.1, 0.1),
                        PointCoordinate(0.4, 0.1),
                        PointCoordinate(0.4, 0.3),
                        PointCoordinate(0.1, 0.3),
                        PointCoordinate(0.1, 0.1)  # Close the polygon
                    ]
                ],
                [
                    # Second rectangle
                    [
                        PointCoordinate(0.5, 0.5),
                        PointCoordinate(0.7, 0.5),
                        PointCoordinate(0.7, 0.7),
                        PointCoordinate(0.5, 0.7),
                        PointCoordinate(0.5, 0.5)  # Close the polygon
                    ]
                ],
            ]),
            "correction_type": "English corrections",
            "checklist_options": "en-ca, en-gb",
            "text_correction": "Fixed typo in English text.",
        }
    },
    "dracula.pdf": {
        # Specify the page number in the PDF. In the example below, page number 17 is labeled
        17: {
            "label_ref": "pdf_label_002",
            "coordinates": PolygonCoordinates(polygons=[
                [
                    # First rectangle
                    [
                        PointCoordinate(0.2, 0.2),
                        PointCoordinate(0.5, 0.2),
                        PointCoordinate(0.5, 0.4),
                        PointCoordinate(0.2, 0.4),
                        PointCoordinate(0.2, 0.2)  # Close the polygon
                    ]
                ],
                [
                    # Second rectangle
                    [
                        PointCoordinate(0.6, 0.6),
                        PointCoordinate(0.8, 0.6),
                        PointCoordinate(0.8, 0.8),
                        PointCoordinate(0.6, 0.8),
                        PointCoordinate(0.6, 0.6)  # Close the polygon
                    ]
                ],
            ]),
            "correction_type": "繁體中文修正",
            "checklist_options": "zh-tw",
            "text_correction": "修正了中文繁體的標點符號。",
        }
    }
}

# Loop through each data unit (image, video, etc.)
for data_unit, frame_coordinates in pdf_labels.items():
    object_instances_by_label_ref = {}

    # Get the label row for the current data unit
    label_row = project.list_label_rows_v2(data_title_eq=data_unit)[0]
    label_row.initialise_labels()

    # Loop through the frames for the current data unit
    for frame_number, items in frame_coordinates.items():
        if not isinstance(items, list):  # Single or multiple objects
            items = [items]

        for item in items:
            label_ref = item["label_ref"]
            coord = item["coordinates"]
            correction_type = item["correction_type"]
            checklist_options_str = item.get("checklist_options", "")
            text_correction = item.get("text_correction", "")

            # Check if label_ref already exists for reusability
            if label_ref not in object_instances_by_label_ref:
                polygon_object_instance: ObjectInstance = polygon_ontology_object.create_instance()
                object_instances_by_label_ref[label_ref] = polygon_object_instance  # Store for reuse

                # Set correction type (radio attribute)
                if correction_type == "English corrections":
                    polygon_object_instance.set_answer(attribute=correction_radio_attribute, answer=english_correction_option)

                    # Set checklist options for English
                    checklist_answers = []
                    for option in [opt.strip() for opt in checklist_options_str.split(",")]:
                        if option == "en-ca":
                            checklist_answers.append(en_ca_option)
                        elif option == "en-gb":
                            checklist_answers.append(en_gb_option)
                        elif option == "en-us":
                            checklist_answers.append(en_us_option)

                    if checklist_answers:
                        polygon_object_instance.set_answer(
                            attribute=english_checklist_attribute,
                            answer=checklist_answers,
                            overwrite=True,
                        )

                    # Set text correction
                    if text_correction:
                        polygon_object_instance.set_answer(attribute=english_correction_text_attribute, answer=text_correction)

                elif correction_type == "繁體中文修正":
                    polygon_object_instance.set_answer(attribute=correction_radio_attribute, answer=chinese_correction_option)

                    # Set checklist options for Chinese
                    checklist_answers = []
                    for option in [opt.strip() for opt in checklist_options_str.split(",")]:
                        if option == "zh-tw":
                            checklist_answers.append(zh_tw_option)
                        elif option == "zh-hk":
                            checklist_answers.append(zh_hk_option)

                    if checklist_answers:
                        polygon_object_instance.set_answer(
                            attribute=chinese_checklist_attribute,
                            answer=checklist_answers,
                            overwrite=True,
                        )

                    # Set text correction
                    if text_correction:
                        polygon_object_instance.set_answer(attribute=chinese_correction_text_attribute, answer=text_correction)

            else:
                # Reuse existing instance across frames
                polygon_object_instance = object_instances_by_label_ref[label_ref]

            # Assign the object to the frame and track it
            polygon_object_instance.set_for_frames(coordinates=coord, frames=frame_number)

    # Add object instances to label_row **only if they have frames assigned**
    for polygon_object_instance in object_instances_by_label_ref.values():
        if polygon_object_instance.get_annotation_frames():  # Ensures it has at least one frame
            label_row.add_object_instance(polygon_object_instance)

    # Upload all labels for this data unit (video/image) to the server
    label_row.save()

print("Labels with English and Mandarin corrections have been added for all data units.")

Classifications with PDFs

The following screenshot provides the Ontology used with the code example.

Classifications and PDFs
# Import dependencies
from pathlib import Path
from encord import EncordUserClient
from encord.objects import Classification, Option

# Configuration
SSH_PATH = "/Users/chris-encord/ssh-private-key.txt"
PROJECT_ID = "9ae72230-c686-484b-a6b9-9ed631c6dce8"

# Create user client using SSH key
user_client = EncordUserClient.create_with_ssh_private_key(
    ssh_private_key_path=SSH_PATH,
    # For US platform users use "https://api.us.encord.com"
    domain="https://api.encord.com",
)

# Get Project
project = user_client.get_project(PROJECT_ID)

# Define specific configurations for each data unit
data_unit_configs = [
    {
        "title": "anne-of-green-gables.pdf",
        "classifications": [
            {
                "name": "Accuracy",
                "type": "radio",
                "option": "5",
                "frame_range": (10, 20)
            },
            {
                "name": "Doc qualities",
                "type": "checklist",
                "options": ["Clarity", "Engaging"],
                "frame_range": (15, 25)
            }
        ]
    },
    {
        "title": "the-legend-of-sleepy-hollow.pdf",
        "classifications": [
            {
                "name": "Accuracy",
                "type": "radio",
                "option": "3",
                "frame_range": (5, 15)
            },
            {
                "name": "Edit",
                "type": "text",
                "text": "Needs more context",
                "frame_range": (3, 10)
            }
        ]
    },
    {
        "title": "the-iliad.pdf",
        "classifications": [
            {
                "name": "Doc qualities",
                "type": "checklist",
                "options": ["Logical structure", "Audience focused"],
                "frame_range": (30, 40)
            }
        ]
    }
]

# Process each data unit with its specific configurations
for config in data_unit_configs:
    try:
        # Get the label row for the specific data unit
        label_rows = project.list_label_rows_v2(
            data_title_eq=config["title"]
        )
        
        if not label_rows:
            print(f"No label row found for {config['title']}, skipping...")
            continue
            
        label_row = label_rows[0]

        # Download the existing labels
        label_row.initialise_labels()
        
        # Get the ontology structure
        ontology_structure = label_row.ontology_structure
        
        # Process each classification for this data unit
        for classification_config in config["classifications"]:
            # Find the classification in the ontology
            classification = ontology_structure.get_child_by_title(
                title=classification_config["name"],
                type_=Classification
            )
            
            # Create classification instance
            classification_instance = classification.create_instance()
            
            # Set the answer based on classification type
            if classification_config["type"] == "radio":
                # For radio button classification
                option = classification.get_child_by_title(
                    title=classification_config["option"],
                    type_=Option
                )
                classification_instance.set_answer(option)
                
            elif classification_config["type"] == "checklist":
                # For checklist classification
                options = []
                for option_name in classification_config["options"]:
                    option = classification.get_child_by_title(
                        title=option_name,
                        type_=Option
                    )
                    options.append(option)
                classification_instance.set_answer(options)
                
            elif classification_config["type"] == "text":
                # For text classification
                classification_instance.set_answer(answer=classification_config["text"])
            
            # Apply to the specified frame range
            start_frame, end_frame = classification_config["frame_range"]
            for frame in range(start_frame, end_frame + 1):
                classification_instance.set_for_frames(
                    frames=frame,
                    manual_annotation=True,
                    confidence=1.0
                )
            
            # Add to label row
            label_row.add_classification_instance(classification_instance)
        
        # Save the changes
        label_row.save()
        print(f"Classifications successfully applied to {config['title']}")
    
    except Exception as e:
        print(f"Error processing {config['title']}: {e}")

Get Started

General

Index

Ontologies

Projects

Labels

Datasets

DICOM

Active API & SDK

PDF Labels and Classifications

Bounding Boxes and PDFs

Polygons and PDFs

Classifications with PDFs

Get Started

General

Index

Ontologies

Projects

Labels

Datasets

DICOM

Active API & SDK

​Bounding Boxes and PDFs

​Polygons and PDFs

​Classifications with PDFs

Bounding Boxes and PDFs

Polygons and PDFs

Classifications with PDFs