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…block Stitch ocr detections workflow block
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| __version__ = "0.24.0" | ||
| __version__ = "0.25.0" | ||
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| if __name__ == "__main__": | ||
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inference/core/workflows/core_steps/transformations/stitch_ocr_detections/v1.py
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| from enum import Enum | ||
| from typing import Dict, List, Literal, Optional, Tuple, Type, Union | ||
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| import numpy as np | ||
| import supervision as sv | ||
| from pydantic import AliasChoices, ConfigDict, Field, field_validator | ||
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| from inference.core.workflows.execution_engine.entities.base import ( | ||
| Batch, | ||
| OutputDefinition, | ||
| ) | ||
| from inference.core.workflows.execution_engine.entities.types import ( | ||
| INTEGER_KIND, | ||
| OBJECT_DETECTION_PREDICTION_KIND, | ||
| STRING_KIND, | ||
| StepOutputSelector, | ||
| WorkflowParameterSelector, | ||
| ) | ||
| from inference.core.workflows.prototypes.block import ( | ||
| BlockResult, | ||
| WorkflowBlock, | ||
| WorkflowBlockManifest, | ||
| ) | ||
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| LONG_DESCRIPTION = """ | ||
| Combines OCR detection results into a coherent text string by organizing detections spatially. | ||
| This transformation is perfect for turning individual OCR results into structured, readable text! | ||
| #### How It Works | ||
| This transformation reconstructs the original text from OCR detection results by: | ||
| 1. 📐 **Grouping** text detections into rows based on their vertical (`y`) positions | ||
| 2. 📏 **Sorting** detections within each row by horizontal (`x`) position | ||
| 3. 📜 **Concatenating** the text in reading order (left-to-right, top-to-bottom) | ||
| #### Parameters | ||
| - **`tolerance`**: Controls how close detections need to be vertically to be considered part of the same line of text. | ||
| A higher tolerance will group detections that are further apart vertically. | ||
| - **`reading_direction`**: Determines the order in which text is read. Available options: | ||
| * **"left_to_right"**: Standard left-to-right reading (e.g., English) ➡️ | ||
| * **"right_to_left"**: Right-to-left reading (e.g., Arabic) ⬅️ | ||
| * **"vertical_top_to_bottom"**: Vertical reading from top to bottom ⬇️ | ||
| * **"vertical_bottom_to_top"**: Vertical reading from bottom to top ⬆️ | ||
| #### Why Use This Transformation? | ||
| This is especially useful for: | ||
| - 📖 Converting individual character/word detections into a readable text block | ||
| - 📝 Reconstructing multi-line text from OCR results | ||
| - 🔀 Maintaining proper reading order for detected text elements | ||
| - 🌏 Supporting different writing systems and text orientations | ||
| #### Example Usage | ||
| Use this transformation after an OCR model that outputs individual words or characters, so you can reconstruct the | ||
| original text layout in its intended format. | ||
| """ | ||
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| SHORT_DESCRIPTION = "Combines OCR detection results into a coherent text string by organizing detections spatially." | ||
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| class ReadingDirection(str, Enum): | ||
| LEFT_TO_RIGHT = "left_to_right" | ||
| RIGHT_TO_LEFT = "right_to_left" | ||
| VERTICAL_TOP_TO_BOTTOM = "vertical_top_to_bottom" | ||
| VERTICAL_BOTTOM_TO_TOP = "vertical_bottom_to_top" | ||
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| class BlockManifest(WorkflowBlockManifest): | ||
| model_config = ConfigDict( | ||
| json_schema_extra={ | ||
| "name": "Stitch OCR Detections", | ||
| "version": "v1", | ||
| "short_description": SHORT_DESCRIPTION, | ||
| "long_description": LONG_DESCRIPTION, | ||
| "license": "Apache-2.0", | ||
| "block_type": "transformation", | ||
| "ui_manifest": { | ||
| "section": "advanced", | ||
| "icon": "fal fa-reel", | ||
| "blockPriority": 2, | ||
| }, | ||
| } | ||
| ) | ||
| type: Literal["roboflow_core/stitch_ocr_detections@v1"] | ||
| predictions: StepOutputSelector( | ||
| kind=[ | ||
| OBJECT_DETECTION_PREDICTION_KIND, | ||
| ] | ||
| ) = Field( | ||
| title="OCR Detections", | ||
| description="The output of an OCR detection model.", | ||
| examples=["$steps.my_ocr_detection_model.predictions"], | ||
| ) | ||
| reading_direction: Literal[ | ||
| "left_to_right", | ||
| "right_to_left", | ||
| "vertical_top_to_bottom", | ||
| "vertical_bottom_to_top", | ||
| ] = Field( | ||
| title="Reading Direction", | ||
| description="The direction of the text in the image.", | ||
| examples=["right_to_left"], | ||
| json_schema_extra={ | ||
| "values_metadata": { | ||
| "left_to_right": { | ||
| "name": "Left To Right", | ||
| "description": "Standard left-to-right reading (e.g., English language)", | ||
| }, | ||
| "right_to_left": { | ||
| "name": "Right To Left", | ||
| "description": "Right-to-left reading (e.g., Arabic)", | ||
| }, | ||
| "vertical_top_to_bottom": { | ||
| "name": "Top To Bottom (Vertical)", | ||
| "description": "Vertical reading from top to bottom", | ||
| }, | ||
| "vertical_bottom_to_top": { | ||
| "name": "Bottom To Top (Vertical)", | ||
| "description": "Vertical reading from bottom to top", | ||
| }, | ||
| } | ||
| }, | ||
| ) | ||
| tolerance: Union[int, WorkflowParameterSelector(kind=[INTEGER_KIND])] = Field( | ||
| title="Tolerance", | ||
| description="The tolerance for grouping detections into the same line of text.", | ||
| default=10, | ||
| examples=[10, "$inputs.tolerance"], | ||
| ) | ||
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| @field_validator("tolerance") | ||
| @classmethod | ||
| def ensure_tolerance_greater_than_zero( | ||
| cls, value: Union[int, str] | ||
| ) -> Union[int, str]: | ||
| if isinstance(value, int) and value <= 0: | ||
| raise ValueError( | ||
| "Stitch OCR detections block expects `tollerance` to be greater than zero." | ||
| ) | ||
| return value | ||
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| @classmethod | ||
| def accepts_batch_input(cls) -> bool: | ||
| return True | ||
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| @classmethod | ||
| def describe_outputs(cls) -> List[OutputDefinition]: | ||
| return [ | ||
| OutputDefinition(name="ocr_text", kind=[STRING_KIND]), | ||
| ] | ||
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| @classmethod | ||
| def get_execution_engine_compatibility(cls) -> Optional[str]: | ||
| return ">=1.0.0,<2.0.0" | ||
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| class StitchOCRDetectionsBlockV1(WorkflowBlock): | ||
| @classmethod | ||
| def get_manifest(cls) -> Type[WorkflowBlockManifest]: | ||
| return BlockManifest | ||
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| def run( | ||
| self, | ||
| predictions: Batch[sv.Detections], | ||
| reading_direction: str, | ||
| tolerance: int, | ||
| ) -> BlockResult: | ||
| return [ | ||
| stitch_ocr_detections( | ||
| detections=detections, | ||
| reading_direction=reading_direction, | ||
| tolerance=tolerance, | ||
| ) | ||
| for detections in predictions | ||
| ] | ||
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| def stitch_ocr_detections( | ||
| detections: sv.Detections, | ||
| reading_direction: str = "left_to_right", | ||
| tolerance: int = 10, | ||
| ) -> Dict[str, str]: | ||
| """ | ||
| Stitch OCR detections into coherent text based on spatial arrangement. | ||
| Args: | ||
| detections: Supervision Detections object containing OCR results | ||
| reading_direction: Direction to read text ("left_to_right", "right_to_left", | ||
| "vertical_top_to_bottom", "vertical_bottom_to_top") | ||
| tolerance: Vertical tolerance for grouping text into lines | ||
| Returns: | ||
| Dict containing stitched OCR text under 'ocr_text' key | ||
| """ | ||
| if len(detections) == 0: | ||
| return {"ocr_text": ""} | ||
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| xyxy = detections.xyxy.round().astype(dtype=int) | ||
| class_names = detections.data["class_name"] | ||
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| # Prepare coordinates based on reading direction | ||
| xyxy = prepare_coordinates(xyxy, reading_direction) | ||
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| # Group detections into lines | ||
| boxes_by_line = group_detections_by_line(xyxy, reading_direction, tolerance) | ||
| # Sort lines based on reading direction | ||
| lines = sorted( | ||
| boxes_by_line.keys(), reverse=reading_direction in ["vertical_bottom_to_top"] | ||
| ) | ||
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| # Build final text | ||
| ordered_class_names = [] | ||
| for i, key in enumerate(lines): | ||
| line_data = boxes_by_line[key] | ||
| line_xyxy = np.array(line_data["xyxy"]) | ||
| line_idx = np.array(line_data["idx"]) | ||
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| # Sort detections within line | ||
| sort_idx = sort_line_detections(line_xyxy, reading_direction) | ||
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| # Add sorted class names for this line | ||
| ordered_class_names.extend(class_names[line_idx[sort_idx]]) | ||
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| # Add line separator if not last line | ||
| if i < len(lines) - 1: | ||
| ordered_class_names.append(get_line_separator(reading_direction)) | ||
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| return {"ocr_text": "".join(ordered_class_names)} | ||
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| def prepare_coordinates( | ||
| xyxy: np.ndarray, | ||
| reading_direction: str, | ||
| ) -> np.ndarray: | ||
| """Prepare coordinates based on reading direction.""" | ||
| if reading_direction in ["vertical_top_to_bottom", "vertical_bottom_to_top"]: | ||
| # Swap x and y coordinates: [x1,y1,x2,y2] -> [y1,x1,y2,x2] | ||
| return xyxy[:, [1, 0, 3, 2]] | ||
| return xyxy | ||
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| def group_detections_by_line( | ||
| xyxy: np.ndarray, | ||
| reading_direction: str, | ||
| tolerance: int, | ||
| ) -> Dict[float, Dict[str, List]]: | ||
| """Group detections into lines based on primary coordinate.""" | ||
| # After prepare_coordinates swap, we always group by y ([:, 1]) | ||
| primary_coord = xyxy[:, 1] # This is y for horizontal, swapped x for vertical | ||
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| # Round primary coordinate to group into lines | ||
| rounded_primary = np.round(primary_coord / tolerance) * tolerance | ||
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| boxes_by_line = {} | ||
| # Group bounding boxes and associated indices by line | ||
| for i, (bbox, line_pos) in enumerate(zip(xyxy, rounded_primary)): | ||
| if line_pos not in boxes_by_line: | ||
| boxes_by_line[line_pos] = {"xyxy": [bbox], "idx": [i]} | ||
| else: | ||
| boxes_by_line[line_pos]["xyxy"].append(bbox) | ||
| boxes_by_line[line_pos]["idx"].append(i) | ||
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| return boxes_by_line | ||
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| def sort_line_detections( | ||
| line_xyxy: np.ndarray, | ||
| reading_direction: str, | ||
| ) -> np.ndarray: | ||
| """Sort detections within a line based on reading direction.""" | ||
| # After prepare_coordinates swap, we always sort by x ([:, 0]) | ||
| if reading_direction in ["left_to_right", "vertical_top_to_bottom"]: | ||
| return line_xyxy[:, 0].argsort() # Sort by x1 (original x or swapped y) | ||
| else: # right_to_left or vertical_bottom_to_top | ||
| return (-line_xyxy[:, 0]).argsort() # Sort by -x1 (original -x or swapped -y) | ||
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| def get_line_separator(reading_direction: str) -> str: | ||
| """Get the appropriate separator based on reading direction.""" | ||
| return "\n" if reading_direction in ["left_to_right", "right_to_left"] else " " |
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