refactor: ocr modules (#2492)

The purpose of this PR is to refactor OCR-related modules to reduce
unnecessary module imports to avoid potential issues (most likely due to
a "circular import").

### Summary
- add `inference_utils` module
(unstructured/partition/pdf_image/inference_utils.py) to define
unstructured-inference library related utility functions, which will
reduce importing unstructured-inference library functions in other files
- add `conftest.py` in `test_unstructured/partition/pdf_image/`
directory to define fixtures that are available to all tests in the same
directory and its subdirectories

### Testing
CI should pass

CHANGELOG.md

@@ -1,4 +1,4 @@
-## 0.12.4-dev5
+## 0.12.4-dev6
 
 ### Enhancements
 

test_unstructured/partition/pdf_image/conftest.py

@@ -0,0 +1,78 @@
+import pytest
+from unstructured_inference.inference.elements import EmbeddedTextRegion
+
+
+@pytest.fixture()
+def mock_embedded_text_regions():
+    return [
+        EmbeddedTextRegion.from_coords(
+            x1=453.00277777777774,
+            y1=317.319341111111,
+            x2=711.5338541666665,
+            y2=358.28571222222206,
+            text="LayoutParser:",
+        ),
+        EmbeddedTextRegion.from_coords(
+            x1=726.4778125,
+            y1=317.319341111111,
+            x2=760.3308594444444,
+            y2=357.1698966666667,
+            text="A",
+        ),
+        EmbeddedTextRegion.from_coords(
+            x1=775.2748177777777,
+            y1=317.319341111111,
+            x2=917.3579885555555,
+            y2=357.1698966666667,
+            text="Unified",
+        ),
+        EmbeddedTextRegion.from_coords(
+            x1=932.3019468888888,
+            y1=317.319341111111,
+            x2=1071.8426522222221,
+            y2=357.1698966666667,
+            text="Toolkit",
+        ),
+        EmbeddedTextRegion.from_coords(
+            x1=1086.7866105555556,
+            y1=317.319341111111,
+            x2=1141.2105142777777,
+            y2=357.1698966666667,
+            text="for",
+        ),
+        EmbeddedTextRegion.from_coords(
+            x1=1156.154472611111,
+            y1=317.319341111111,
+            x2=1256.334784222222,
+            y2=357.1698966666667,
+            text="Deep",
+        ),
+        EmbeddedTextRegion.from_coords(
+            x1=437.83888888888885,
+            y1=367.13322999999986,
+            x2=610.0171992222222,
+            y2=406.9837855555556,
+            text="Learning",
+        ),
+        EmbeddedTextRegion.from_coords(
+            x1=624.9611575555555,
+            y1=367.13322999999986,
+            x2=741.6754646666665,
+            y2=406.9837855555556,
+            text="Based",
+        ),
+        EmbeddedTextRegion.from_coords(
+            x1=756.619423,
+            y1=367.13322999999986,
+            x2=958.3867708333332,
+            y2=406.9837855555556,
+            text="Document",
+        ),
+        EmbeddedTextRegion.from_coords(
+            x1=973.3307291666665,
+            y1=367.13322999999986,
+            x2=1092.0535042777776,
+            y2=406.9837855555556,
+            text="Image",
+        ),
+    ]

test_unstructured/partition/pdf_image/test_inference_utils.py

@@ -0,0 +1,37 @@
+from unstructured_inference.inference.elements import TextRegion
+from unstructured_inference.inference.layoutelement import LayoutElement
+
+from unstructured.documents.elements import ElementType
+from unstructured.partition.pdf_image.inference_utils import (
+    build_layout_elements_from_ocr_regions,
+    merge_text_regions,
+)
+
+
+def test_merge_text_regions(mock_embedded_text_regions):
+    expected = TextRegion.from_coords(
+        x1=437.83888888888885,
+        y1=317.319341111111,
+        x2=1256.334784222222,
+        y2=406.9837855555556,
+        text="LayoutParser: A Unified Toolkit for Deep Learning Based Document Image",
+    )
+
+    merged_text_region = merge_text_regions(mock_embedded_text_regions)
+    assert merged_text_region == expected
+
+
+def test_build_layout_elements_from_ocr_regions(mock_embedded_text_regions):
+    expected = [
+        LayoutElement.from_coords(
+            x1=437.83888888888885,
+            y1=317.319341111111,
+            x2=1256.334784222222,
+            y2=406.9837855555556,
+            text="LayoutParser: A Unified Toolkit for Deep Learning Based Document Image",
+            type=ElementType.UNCATEGORIZED_TEXT,
+        ),
+    ]
+
+    elements = build_layout_elements_from_ocr_regions(mock_embedded_text_regions)
+    assert elements == expected

test_unstructured/partition/pdf_image/test_ocr.py

@@ -18,10 +18,6 @@ from unstructured.partition.pdf_image.ocr import pad_element_bboxes
 from unstructured.partition.utils.constants import (
     Source,
 )
-from unstructured.partition.utils.ocr_models.ocr_interface import (
-    get_elements_from_ocr_regions,
-    merge_text_regions,
-)
 from unstructured.partition.utils.ocr_models.paddle_ocr import OCRAgentPaddle
 from unstructured.partition.utils.ocr_models.tesseract_ocr import (
     OCRAgentTesseract,
@@ -231,35 +227,6 @@ def test_aggregate_ocr_text_by_block():
     assert text == expected
 
 
-def test_merge_text_regions(mock_embedded_text_regions):
-    expected = TextRegion.from_coords(
-        x1=437.83888888888885,
-        y1=317.319341111111,
-        x2=1256.334784222222,
-        y2=406.9837855555556,
-        text="LayoutParser: A Unified Toolkit for Deep Learning Based Document Image",
-    )
-
-    merged_text_region = merge_text_regions(mock_embedded_text_regions)
-    assert merged_text_region == expected
-
-
-def test_get_elements_from_ocr_regions(mock_embedded_text_regions):
-    expected = [
-        LayoutElement.from_coords(
-            x1=437.83888888888885,
-            y1=317.319341111111,
-            x2=1256.334784222222,
-            y2=406.9837855555556,
-            text="LayoutParser: A Unified Toolkit for Deep Learning Based Document Image",
-            type=ElementType.UNCATEGORIZED_TEXT,
-        ),
-    ]
-
-    elements = get_elements_from_ocr_regions(mock_embedded_text_regions)
-    assert elements == expected
-
-
 @pytest.mark.parametrize("zoom", [1, 0.1, 5, -1, 0])
 def test_zoom_image(zoom):
     image = Image.new("RGB", (100, 100))
@@ -280,82 +247,6 @@ def mock_layout(mock_embedded_text_regions):
     ]
 
 
-@pytest.fixture()
-def mock_embedded_text_regions():
-    return [
-        EmbeddedTextRegion.from_coords(
-            x1=453.00277777777774,
-            y1=317.319341111111,
-            x2=711.5338541666665,
-            y2=358.28571222222206,
-            text="LayoutParser:",
-        ),
-        EmbeddedTextRegion.from_coords(
-            x1=726.4778125,
-            y1=317.319341111111,
-            x2=760.3308594444444,
-            y2=357.1698966666667,
-            text="A",
-        ),
-        EmbeddedTextRegion.from_coords(
-            x1=775.2748177777777,
-            y1=317.319341111111,
-            x2=917.3579885555555,
-            y2=357.1698966666667,
-            text="Unified",
-        ),
-        EmbeddedTextRegion.from_coords(
-            x1=932.3019468888888,
-            y1=317.319341111111,
-            x2=1071.8426522222221,
-            y2=357.1698966666667,
-            text="Toolkit",
-        ),
-        EmbeddedTextRegion.from_coords(
-            x1=1086.7866105555556,
-            y1=317.319341111111,
-            x2=1141.2105142777777,
-            y2=357.1698966666667,
-            text="for",
-        ),
-        EmbeddedTextRegion.from_coords(
-            x1=1156.154472611111,
-            y1=317.319341111111,
-            x2=1256.334784222222,
-            y2=357.1698966666667,
-            text="Deep",
-        ),
-        EmbeddedTextRegion.from_coords(
-            x1=437.83888888888885,
-            y1=367.13322999999986,
-            x2=610.0171992222222,
-            y2=406.9837855555556,
-            text="Learning",
-        ),
-        EmbeddedTextRegion.from_coords(
-            x1=624.9611575555555,
-            y1=367.13322999999986,
-            x2=741.6754646666665,
-            y2=406.9837855555556,
-            text="Based",
-        ),
-        EmbeddedTextRegion.from_coords(
-            x1=756.619423,
-            y1=367.13322999999986,
-            x2=958.3867708333332,
-            y2=406.9837855555556,
-            text="Document",
-        ),
-        EmbeddedTextRegion.from_coords(
-            x1=973.3307291666665,
-            y1=367.13322999999986,
-            x2=1092.0535042777776,
-            y2=406.9837855555556,
-            text="Image",
-        ),
-    ]
-
-
 def test_supplement_layout_with_ocr_elements(mock_layout, mock_ocr_regions):
     ocr_elements = [
         LayoutElement(text=r.text, source=None, type=ElementType.UNCATEGORIZED_TEXT, bbox=r.bbox)

unstructured/__version__.py

@@ -1 +1 @@
-__version__ = "0.12.4-dev5"  # pragma: no cover
+__version__ = "0.12.4-dev6"  # pragma: no cover

unstructured/partition/pdf.py

@@ -872,7 +872,6 @@ def convert_pdf_to_images(
             yield image
 
 
-@requires_dependencies("unstructured_pytesseract", "unstructured_inference")
 def _partition_pdf_or_image_with_ocr(
     filename: str = "",
     file: Optional[Union[bytes, IO[bytes]]] = None,

unstructured/partition/pdf_image/inference_utils.py

@@ -0,0 +1,114 @@
+from typing import TYPE_CHECKING, List, Optional, Union, cast
+
+from unstructured_inference.constants import Source
+from unstructured_inference.inference.elements import TextRegion
+from unstructured_inference.inference.layoutelement import (
+    LayoutElement,
+    partition_groups_from_regions,
+)
+
+from unstructured.documents.elements import ElementType
+
+if TYPE_CHECKING:
+    from unstructured_inference.inference.elements import Rectangle
+
+
+def build_text_region_from_coords(
+    x1: Union[int, float],
+    y1: Union[int, float],
+    x2: Union[int, float],
+    y2: Union[int, float],
+    text: Optional[str] = None,
+    source: Optional[Source] = None,
+) -> TextRegion:
+    """"""
+
+    return TextRegion.from_coords(
+        x1,
+        y1,
+        x2,
+        y2,
+        text=text,
+        source=source,
+    )
+
+
+def build_layout_element(
+    bbox: "Rectangle",
+    text: Optional[str] = None,
+    source: Optional[Source] = None,
+    element_type: Optional[str] = None,
+) -> LayoutElement:
+    """"""
+
+    return LayoutElement(bbox=bbox, text=text, source=source, type=element_type)
+
+
+def build_layout_elements_from_ocr_regions(
+    ocr_regions: List[TextRegion],
+    ocr_text: Optional[str] = None,
+    group_by_ocr_text: bool = False,
+) -> List[LayoutElement]:
+    """
+    Get layout elements from OCR regions
+    """
+
+    if group_by_ocr_text:
+        text_sections = ocr_text.split("\n\n")
+        grouped_regions = []
+        for text_section in text_sections:
+            regions = []
+            words = text_section.replace("\n", " ").split()
+            for ocr_region in ocr_regions:
+                if not words:
+                    break
+                if ocr_region.text in words:
+                    regions.append(ocr_region)
+                    words.remove(ocr_region.text)
+
+            if not regions:
+                continue
+
+            for r in regions:
+                ocr_regions.remove(r)
+
+            grouped_regions.append(regions)
+    else:
+        grouped_regions = cast(
+            List[List[TextRegion]],
+            partition_groups_from_regions(ocr_regions),
+        )
+
+    merged_regions = [merge_text_regions(group) for group in grouped_regions]
+    return [
+        build_layout_element(
+            bbox=r.bbox, text=r.text, source=r.source, element_type=ElementType.UNCATEGORIZED_TEXT
+        )
+        for r in merged_regions
+    ]
+
+
+def merge_text_regions(regions: List[TextRegion]) -> TextRegion:
+    """
+    Merge a list of TextRegion objects into a single TextRegion.
+
+    Parameters:
+    - group (List[TextRegion]): A list of TextRegion objects to be merged.
+
+    Returns:
+    - TextRegion: A single merged TextRegion object.
+    """
+
+    if not regions:
+        raise ValueError("The text regions to be merged must be provided.")
+
+    min_x1 = min([tr.bbox.x1 for tr in regions])
+    min_y1 = min([tr.bbox.y1 for tr in regions])
+    max_x2 = max([tr.bbox.x2 for tr in regions])
+    max_y2 = max([tr.bbox.y2 for tr in regions])
+
+    merged_text = " ".join([tr.text for tr in regions if tr.text])
+    sources = [tr.source for tr in regions]
+    source = sources[0] if all(s == sources[0] for s in sources) else None
+
+    return TextRegion.from_coords(min_x1, min_y1, max_x2, max_y2, merged_text, source)

unstructured/partition/pdf_image/ocr.py

@@ -1,6 +1,6 @@
 import os
 import tempfile
-from typing import BinaryIO, Dict, List, Optional, Union, cast
+from typing import TYPE_CHECKING, BinaryIO, Dict, List, Optional, Union, cast
 
 import pdf2image
 
@@ -8,12 +8,6 @@ import pdf2image
 # unstructured.documents.elements.Image
 from PIL import Image as PILImage
 from PIL import ImageSequence
-from unstructured_inference.inference.elements import TextRegion
-from unstructured_inference.inference.layout import DocumentLayout, PageLayout
-from unstructured_inference.inference.layoutelement import (
-    LayoutElement,
-)
-from unstructured_inference.models.tables import UnstructuredTableTransformerModel
 
 from unstructured.documents.elements import ElementType
 from unstructured.logger import logger
@@ -29,8 +23,15 @@ from unstructured.partition.utils.constants import (
 )
 from unstructured.partition.utils.ocr_models.ocr_interface import (
     OCRAgent,
-    get_elements_from_ocr_regions,
 )
+from unstructured.utils import requires_dependencies
+
+if TYPE_CHECKING:
+    from unstructured_inference.inference.elements import TextRegion
+    from unstructured_inference.inference.layout import DocumentLayout, PageLayout
+    from unstructured_inference.inference.layoutelement import LayoutElement
+    from unstructured_inference.models.tables import UnstructuredTableTransformerModel
+
 
 # Force tesseract to be single threaded,
 # otherwise we see major performance problems
@@ -91,6 +92,7 @@ def process_data_with_ocr(
         return merged_layouts
 
 
+@requires_dependencies("unstructured_inference")
 def process_file_with_ocr(
     filename: str,
     out_layout: "DocumentLayout",
@@ -127,6 +129,9 @@ def process_file_with_ocr(
     Returns:
         DocumentLayout: The merged layout information obtained after OCR processing.
     """
+
+    from unstructured_inference.inference.layout import DocumentLayout
+
     merged_page_layouts = []
     try:
         if is_image:
@@ -175,6 +180,7 @@ def process_file_with_ocr(
             raise FileNotFoundError(f'File "{filename}" not found!') from e
 
 
+@requires_dependencies("unstructured_inference")
 def supplement_page_layout_with_ocr(
     page_layout: "PageLayout",
     image: PILImage,
@@ -198,7 +204,7 @@ def supplement_page_layout_with_ocr(
             ocr_languages=ocr_languages,
         )
         page_layout.elements[:] = merge_out_layout_with_ocr_layout(
-            out_layout=cast(List[LayoutElement], page_layout.elements),
+            out_layout=cast(List["LayoutElement"], page_layout.elements),
             ocr_layout=ocr_layout,
         )
     elif ocr_mode == OCRMode.INDIVIDUAL_BLOCKS.value:
@@ -236,7 +242,7 @@ def supplement_page_layout_with_ocr(
             raise RuntimeError("Unable to load table extraction agent.")
 
         page_layout.elements[:] = supplement_element_with_table_extraction(
-            elements=cast(List[LayoutElement], page_layout.elements),
+            elements=cast(List["LayoutElement"], page_layout.elements),
             image=image,
             tables_agent=tables.tables_agent,
             ocr_languages=ocr_languages,
@@ -248,13 +254,13 @@ def supplement_page_layout_with_ocr(
 
 
 def supplement_element_with_table_extraction(
-    elements: List[LayoutElement],
+    elements: List["LayoutElement"],
     image: PILImage,
     tables_agent: "UnstructuredTableTransformerModel",
     ocr_languages: str = "eng",
     ocr_agent: OCRAgent = OCRAgent.get_instance(OCR_AGENT_TESSERACT),
     extracted_regions: Optional[List["TextRegion"]] = None,
-) -> List[LayoutElement]:
+) -> List["LayoutElement"]:
     """Supplement the existing layout with table extraction. Any Table elements
     that are extracted will have a metadata field "text_as_html" where
     the table's text content is rendered into an html string.
@@ -324,10 +330,10 @@ def get_table_tokens(
 
 
 def merge_out_layout_with_ocr_layout(
-    out_layout: List[LayoutElement],
+    out_layout: List["LayoutElement"],
-    ocr_layout: List[TextRegion],
+    ocr_layout: List["TextRegion"],
     supplement_with_ocr_elements: bool = True,
-) -> List[LayoutElement]:
+) -> List["LayoutElement"]:
     """
     Merge the out layout with the OCR-detected text regions on page level.
 
@@ -356,8 +362,8 @@ def merge_out_layout_with_ocr_layout(
 
 
 def aggregate_ocr_text_by_block(
-    ocr_layout: List[TextRegion],
+    ocr_layout: List["TextRegion"],
-    region: TextRegion,
+    region: "TextRegion",
     subregion_threshold: float,
 ) -> Optional[str]:
     """Extracts the text aggregated from the regions of the ocr layout that lie within the given
@@ -376,10 +382,11 @@ def aggregate_ocr_text_by_block(
     return " ".join(extracted_texts) if extracted_texts else ""
 
 
+@requires_dependencies("unstructured_inference")
 def supplement_layout_with_ocr_elements(
-    layout: List[LayoutElement],
+    layout: List["LayoutElement"],
-    ocr_layout: List[TextRegion],
+    ocr_layout: List["TextRegion"],
-) -> List[LayoutElement]:
+) -> List["LayoutElement"]:
     """
     Supplement the existing layout with additional OCR-derived elements.
 
@@ -401,11 +408,16 @@ def supplement_layout_with_ocr_elements(
     Note:
     - The function relies on `is_almost_subregion_of()` method to determine if an OCR region
       is a subregion of an existing layout element.
-    - It also relies on `get_elements_from_ocr_regions()` to convert OCR regions to layout elements.
+    - It also relies on `build_layout_elements_from_ocr_regions()` to convert OCR regions to
+     layout elements.
     - The `SUBREGION_THRESHOLD_FOR_OCR` constant is used to specify the subregion matching
      threshold.
     """
 
+    from unstructured.partition.pdf_image.inference_utils import (
+        build_layout_elements_from_ocr_regions,
+    )
+
     ocr_regions_to_remove = []
     for ocr_region in ocr_layout:
         for el in layout:
@@ -419,7 +431,7 @@ def supplement_layout_with_ocr_elements(
 
     ocr_regions_to_add = [region for region in ocr_layout if region not in ocr_regions_to_remove]
     if ocr_regions_to_add:
-        ocr_elements_to_add = get_elements_from_ocr_regions(ocr_regions_to_add)
+        ocr_elements_to_add = build_layout_elements_from_ocr_regions(ocr_regions_to_add)
         final_layout = layout + ocr_elements_to_add
     else:
         final_layout = layout
@@ -427,7 +439,7 @@ def supplement_layout_with_ocr_elements(
     return final_layout
 
 
-def get_ocr_agent() -> str:
+def get_ocr_agent() -> OCRAgent:
     ocr_agent_module = env_config.OCR_AGENT
     message = (
         "OCR agent name %s is outdated and will be deprecated in a future release; please use %s "

unstructured/partition/utils/ocr_models/ocr_interface.py

@@ -1,18 +1,17 @@
 import functools
 import importlib
 from abc import ABC, abstractmethod
-from typing import Any, List, Optional, cast
+from typing import TYPE_CHECKING, Any, List
 
-from PIL import Image as PILImage
+from unstructured.partition.utils.constants import OCR_AGENT_MODULES_WHITELIST
+
+if TYPE_CHECKING:
+    from PIL import PILImage
     from unstructured_inference.inference.elements import TextRegion
     from unstructured_inference.inference.layoutelement import (
         LayoutElement,
-    partition_groups_from_regions,
     )
 
-from unstructured.documents.elements import ElementType
-from unstructured.partition.utils.constants import OCR_AGENT_MODULES_WHITELIST
-
 
 class OCRAgent(ABC):
     def __init__(self):
@@ -27,19 +26,19 @@ class OCRAgent(ABC):
         pass
 
     @abstractmethod
-    def get_text_from_image(self, image: PILImage, ocr_languages: str = "eng") -> str:
+    def get_text_from_image(self, image: "PILImage", ocr_languages: str = "eng") -> str:
         pass
 
     @abstractmethod
     def get_layout_from_image(
-        self, image: PILImage, ocr_languages: str = "eng"
+        self, image: "PILImage", ocr_languages: str = "eng"
-    ) -> List[TextRegion]:
+    ) -> List["TextRegion"]:
         pass
 
     @abstractmethod
     def get_layout_elements_from_image(
-        self, image: PILImage, ocr_languages: str = "eng"
+        self, image: "PILImage", ocr_languages: str = "eng"
-    ) -> List[LayoutElement]:
+    ) -> List["LayoutElement"]:
         pass
 
     @staticmethod
@@ -55,73 +54,3 @@ class OCRAgent(ABC):
                 f"Environment variable OCR_AGENT module name {module_name}",
                 f" must be set to a whitelisted module part of {OCR_AGENT_MODULES_WHITELIST}.",
             )
-
-
-def get_elements_from_ocr_regions(
-    ocr_regions: List[TextRegion],
-    ocr_text: Optional[str] = None,
-    group_by_ocr_text: bool = False,
-) -> List[LayoutElement]:
-    """
-    Get layout elements from OCR regions
-    """
-
-    if group_by_ocr_text:
-        text_sections = ocr_text.split("\n\n")
-        grouped_regions = []
-        for text_section in text_sections:
-            regions = []
-            words = text_section.replace("\n", " ").split()
-            for ocr_region in ocr_regions:
-                if not words:
-                    break
-                if ocr_region.text in words:
-                    regions.append(ocr_region)
-                    words.remove(ocr_region.text)
-
-            if not regions:
-                continue
-
-            for r in regions:
-                ocr_regions.remove(r)
-
-            grouped_regions.append(regions)
-    else:
-        grouped_regions = cast(
-            List[List[TextRegion]],
-            partition_groups_from_regions(ocr_regions),
-        )
-
-    merged_regions = [merge_text_regions(group) for group in grouped_regions]
-    return [
-        LayoutElement(
-            text=r.text, source=r.source, type=ElementType.UNCATEGORIZED_TEXT, bbox=r.bbox
-        )
-        for r in merged_regions
-    ]
-
-
-def merge_text_regions(regions: List[TextRegion]) -> TextRegion:
-    """
-    Merge a list of TextRegion objects into a single TextRegion.
-
-    Parameters:
-    - group (List[TextRegion]): A list of TextRegion objects to be merged.
-
-    Returns:
-    - TextRegion: A single merged TextRegion object.
-    """
-
-    if not regions:
-        raise ValueError("The text regions to be merged must be provided.")
-
-    min_x1 = min([tr.bbox.x1 for tr in regions])
-    min_y1 = min([tr.bbox.y1 for tr in regions])
-    max_x2 = max([tr.bbox.x2 for tr in regions])
-    max_y2 = max([tr.bbox.y2 for tr in regions])
-
-    merged_text = " ".join([tr.text for tr in regions if tr.text])
-    sources = [tr.source for tr in regions]
-    source = sources[0] if all(s == sources[0] for s in sources) else None
-
-    return TextRegion.from_coords(min_x1, min_y1, max_x2, max_y2, merged_text, source)

unstructured/partition/utils/ocr_models/paddle_ocr.py

@@ -1,11 +1,7 @@
-from typing import List
+from typing import TYPE_CHECKING, List
 
 import numpy as np
 from PIL import Image as PILImage
-from unstructured_inference.inference.elements import TextRegion
-from unstructured_inference.inference.layoutelement import (
-    LayoutElement,
-)
 
 from unstructured.documents.elements import ElementType
 from unstructured.logger import logger
@@ -14,6 +10,11 @@ from unstructured.partition.utils.constants import (
     Source,
 )
 from unstructured.partition.utils.ocr_models.ocr_interface import OCRAgent
+from unstructured.utils import requires_dependencies
+
+if TYPE_CHECKING:
+    from unstructured_inference.inference.elements import TextRegion
+    from unstructured_inference.inference.layoutelement import LayoutElement
 
 
 class OCRAgentPaddle(OCRAgent):
@@ -63,7 +64,7 @@ class OCRAgentPaddle(OCRAgent):
 
     def get_layout_from_image(
         self, image: PILImage, ocr_languages: str = "eng"
-    ) -> List[TextRegion]:
+    ) -> List["TextRegion"]:
         """Get the OCR regions from image as a list of text regions with paddle."""
 
         logger.info("Processing entire page OCR with paddle...")
@@ -76,9 +77,12 @@ class OCRAgentPaddle(OCRAgent):
 
         return ocr_regions
 
+    @requires_dependencies("unstructured_inference")
     def get_layout_elements_from_image(
         self, image: PILImage, ocr_languages: str = "eng"
-    ) -> List[LayoutElement]:
+    ) -> List["LayoutElement"]:
+        from unstructured.partition.pdf_image.inference_utils import build_layout_element
+
         ocr_regions = self.get_layout_from_image(
             image,
             ocr_languages=ocr_languages,
@@ -88,13 +92,17 @@ class OCRAgentPaddle(OCRAgent):
         # terms of grouping because we get ocr_text from `ocr_layout, so the first two grouping
         # and merging steps are not necessary.
         return [
-            LayoutElement(
+            build_layout_element(
-                bbox=r.bbox, text=r.text, source=r.source, type=ElementType.UNCATEGORIZED_TEXT
+                bbox=r.bbox,
+                text=r.text,
+                source=r.source,
+                element_type=ElementType.UNCATEGORIZED_TEXT,
             )
             for r in ocr_regions
         ]
 
-    def parse_data(self, ocr_data: list) -> List[TextRegion]:
+    @requires_dependencies("unstructured_inference")
+    def parse_data(self, ocr_data: list) -> List["TextRegion"]:
         """
         Parse the OCR result data to extract a list of TextRegion objects from
         paddle.
@@ -114,6 +122,9 @@ class OCRAgentPaddle(OCRAgent):
         - An empty string or a None value for the 'text' key in the input
           dictionary will result in its associated bounding box being ignored.
         """
+
+        from unstructured.partition.pdf_image.inference_utils import build_text_region_from_coords
+
         text_regions = []
         for idx in range(len(ocr_data)):
             res = ocr_data[idx]
@@ -130,12 +141,12 @@ class OCRAgentPaddle(OCRAgent):
                     continue
                 cleaned_text = text.strip()
                 if cleaned_text:
-                    text_region = TextRegion.from_coords(
+                    text_region = build_text_region_from_coords(
                         x1,
                         y1,
                         x2,
                         y2,
-                        cleaned_text,
+                        text=cleaned_text,
                         source=Source.OCR_PADDLE,
                     )
                     text_regions.append(text_region)

unstructured/partition/utils/ocr_models/tesseract_ocr.py

@@ -1,18 +1,13 @@
-from typing import List
+from typing import TYPE_CHECKING, List
 
 import cv2
 import numpy as np
 import pandas as pd
 import unstructured_pytesseract
 from PIL import Image as PILImage
-from unstructured_inference.inference.elements import TextRegion
-from unstructured_inference.inference.layoutelement import (
-    LayoutElement,
-)
 from unstructured_pytesseract import Output
 
 from unstructured.logger import logger
-from unstructured.partition.pdf_image.ocr import get_elements_from_ocr_regions
 from unstructured.partition.utils.config import env_config
 from unstructured.partition.utils.constants import (
     IMAGE_COLOR_DEPTH,
@@ -21,6 +16,13 @@ from unstructured.partition.utils.constants import (
     Source,
 )
 from unstructured.partition.utils.ocr_models.ocr_interface import OCRAgent
+from unstructured.utils import requires_dependencies
+
+if TYPE_CHECKING:
+    from unstructured_inference.inference.elements import TextRegion
+    from unstructured_inference.inference.layoutelement import (
+        LayoutElement,
+    )
 
 
 class OCRAgentTesseract(OCRAgent):
@@ -38,7 +40,7 @@ class OCRAgentTesseract(OCRAgent):
 
     def get_layout_from_image(
         self, image: PILImage, ocr_languages: str = "eng"
-    ) -> List[TextRegion]:
+    ) -> List["TextRegion"]:
         """Get the OCR regions from image as a list of text regions with tesseract."""
 
         logger.info("Processing entire page OCR with tesseract...")
@@ -83,9 +85,14 @@ class OCRAgentTesseract(OCRAgent):
 
         return ocr_regions
 
+    @requires_dependencies("unstructured_inference")
     def get_layout_elements_from_image(
         self, image: PILImage, ocr_languages: str = "eng"
-    ) -> List[LayoutElement]:
+    ) -> List["LayoutElement"]:
+        from unstructured.partition.pdf_image.inference_utils import (
+            build_layout_elements_from_ocr_regions,
+        )
+
         ocr_regions = self.get_layout_from_image(
             image,
             ocr_languages=ocr_languages,
@@ -103,13 +110,14 @@ class OCRAgentTesseract(OCRAgent):
             ocr_languages=ocr_languages,
         )
 
-        return get_elements_from_ocr_regions(
+        return build_layout_elements_from_ocr_regions(
             ocr_regions=ocr_regions,
             ocr_text=ocr_text,
             group_by_ocr_text=True,
         )
 
-    def parse_data(self, ocr_data: pd.DataFrame, zoom: float = 1) -> List[TextRegion]:
+    @requires_dependencies("unstructured_inference")
+    def parse_data(self, ocr_data: pd.DataFrame, zoom: float = 1) -> List["TextRegion"]:
         """
         Parse the OCR result data to extract a list of TextRegion objects from
         tesseract.
@@ -137,6 +145,8 @@ class OCRAgentTesseract(OCRAgent):
           data frame will result in its associated bounding box being ignored.
         """
 
+        from unstructured.partition.pdf_image.inference_utils import build_text_region_from_coords
+
         if zoom <= 0:
             zoom = 1
 
@@ -153,7 +163,7 @@ class OCRAgentTesseract(OCRAgent):
                 y1 = idtx.top / zoom
                 x2 = (idtx.left + idtx.width) / zoom
                 y2 = (idtx.top + idtx.height) / zoom
-                text_region = TextRegion.from_coords(
+                text_region = build_text_region_from_coords(
                     x1,
                     y1,
                     x2,

unstructured/partition/utils/processing_elements.py

@@ -1,11 +1,13 @@
 from collections import defaultdict
-
+from typing import TYPE_CHECKING
-from unstructured_inference.inference.layout import DocumentLayout
 
 from unstructured.partition.utils.constants import Source
 
+if TYPE_CHECKING:
+    from unstructured_inference.inference.layout import DocumentLayout
 
-def clean_pdfminer_inner_elements(document: DocumentLayout) -> DocumentLayout:
+
+def clean_pdfminer_inner_elements(document: "DocumentLayout") -> "DocumentLayout":
     """Clean pdfminer elements from inside tables and stores them in extra_info dictionary
     with the table id as key"""
     defaultdict(list)