| Parameter |
Description |
Type |
Options |
Default Value |
input |
Data to be predicted, supports multiple input types, required. |
Python Var|str|list |
- Python Var: Image data represented by
numpy.ndarray.
- str: Local path of image or PDF files, e.g.,
/root/data/img.jpg; URL link, such as the network URL of an image or PDF file: Example; Local directory, the directory should contain images to be predicted, e.g., /root/data/ (currently, prediction for PDF files in directories is not supported; PDF files must specify the exact file path).
- List: List elements must be of the above types, such as
[numpy.ndarray, numpy.ndarray], [“/root/data/img1.jpg”, “/root/data/img2.jpg”], [“/root/data1”, “/root/data2”].
|
None |
device |
Inference device. |
str|None |
- CPU: Use CPU for inference, e.g.,
cpu.
- GPU: Use the first GPU for inference, e.g.,
gpu:0.
- NPU: Use the first NPU for inference, e.g.,
npu:0.
- XPU: Use the first XPU for inference, e.g.,
xpu:0.
- MLU: Use the first MLU for inference, e.g.,
mlu:0.
- DCU: Use the first DCU for inference, e.g.,
dcu:0.
- None: If set to
None, the default value initialized by the production line will be used. During initialization, the local GPU 0 will be prioritized; if unavailable, the CPU will be used.
|
None |
use_doc_orientation_classify |
Whether to use the document orientation classification module. |
bool|None |
- bool:
True or False.
- None: If set to
None, the default value initialized by the production line will be used, initialized as True.
|
None |
use_doc_unwarping |
Whether to use the document unwarping module. |
bool|None |
- bool:
True or False.
- None: If set to
None, the default value initialized by the production line will be used, initialized as True.
|
None |
use_layout_detection |
Whether to use the layout detection module. |
bool|None |
- bool:
True or False.
- None: If set to
None, the default value initialized by the production line will be used, initialized as True.
|
None |
text_det_limit_side_len |
Image side length limit for text detection |
int|None |
- int: Any integer greater than
0;
- None: If set to
None, the default value initialized in production will be used, initialized as 960;
|
None |
text_det_limit_type |
Type of image side length limit for text detection |
str|None |
- str: Supports
min and max. min ensures the shortest side of the image is not less than det_limit_side_len, while max ensures the longest side is not greater than limit_side_len;
- None: If set to
None, the default value initialized in production will be used, initialized as max;
|
None |
text_det_thresh |
Detection pixel threshold; in the output probability map, pixels with scores greater than this threshold will be considered as text pixels |
float|None |
- float: Any floating-point number greater than
0;
- None: If set to
None, the default value initialized in production will be used, initialized as 0.3;
|
None |
text_det_box_thresh |
Detection box threshold; the average score of all pixels within the detection box must be greater than this threshold for the result to be considered a text region |
float|None |
- float: Any floating-point number greater than
0;
- None: If set to
None, the default value initialized in production will be used, initialized as 0.6;
|
None |
text_det_unclip_ratio |
Text detection expansion ratio; this value determines the extent of expansion of the text region, with larger values resulting in greater expansion |
float|None |
- float: Any floating-point number greater than
0;
- None: If set to
None, the default value initialized in production will be used, initialized as 2.0;
|
None |
text_rec_score_thresh |
Text recognition threshold; text results with scores greater than this threshold will be retained |
float|None |
- float: Any floating-point number greater than
0;
- None: If set to
None, the default value initialized in production will be used, initialized as 0.0, meaning no threshold is set;
|
None |
use_table_cells_ocr_results |
Whether to enable Table-Cells-OCR mode, when not enabled, use global OCR result to fill to HTML table, when enabled, do OCR cell by cell and fill to HTML table (it will increase the time consuming). Both of them perform differently in different scenarios, please choose according to the actual situation. |
bool |
- bool：
True or False
False |
use_e2e_wired_table_rec_model |
Whether to enable the wired table end-to-end prediction mode, when not enabled, using the table cells detection model prediction results filled to the HTML table, when enabled, using the end-to-end table structure recognition model cell prediction results filled to the HTML table. Both of them have different performance in different scenarios, please choose according to the actual situation. |
bool |
- bool：
True or False
False |
use_e2e_wireless_table_rec_model |
Whether to enable the wireless table end-to-end prediction mode, when not enabled, using the table cells detection model prediction results filled to the HTML table, when enabled, using the end-to-end table structure recognition model cell prediction results filled to the HTML table. Both of them have different performance in different scenarios, please choose according to the actual situation. |
bool |
| | |
| Method |
Method Description |
Parameter |
Parameter Type |
Parameter Description |
Default Value |
print() |
Print the result to the terminal |
format_json |
bool |
Whether to format the output content using JSON indentation |
True |
indent |
int |
Specify the indentation level to beautify the JSON data, making it more readable. Only effective when format_json is True |
4 |
ensure_ascii |
bool |
Control whether non-ASCII characters are escaped to Unicode. If set to True, all non-ASCII characters will be escaped; False retains the original characters. Only effective when format_json is True |
False |
save_to_json() |
Save the result as a JSON file |
save_path |
str |
The file path for saving. If it is a directory, the saved file will have the same name as the input file type |
None |
indent |
int |
Specify the indentation level to beautify the JSON data, making it more readable. Only effective when format_json is True |
4 |
ensure_ascii |
bool |
Control whether non-ASCII characters are escaped to Unicode. If set to True, all non-ASCII characters will be escaped; False retains the original characters. Only effective when format_json is True |
False |
save_to_img() |
Save the result as an image file |
save_path |
str |
The file path for saving, supporting both directory and file paths |
None |
save_to_xlsx() |
Save the result as an xlsx file |
save_path |
str |
The file path for saving, supporting both directory and file paths |
None |
save_to_html() |
Save the result as an HTML file |
save_path |
str |
The file path for saving, supporting both directory and file paths |
None |
- Calling the `print()` method will print the results to the terminal, and the content printed to the terminal is explained as follows:
- `input_path`: `(str)` The input path of the image to be predicted.
- `page_index`: `(Union[int, None])` If the input is a PDF file, it indicates which page of the PDF is currently being processed; otherwise, it is `None`.
- `model_settings`: `(Dict[str, bool])` Configuration parameters for the pipeline models.
- `use_doc_preprocessor`: `(bool)` Controls whether to enable the document preprocessing sub-line.
- `use_layout_detection`: `(bool)` Controls whether to enable the layout detection sub-line.
- `use_ocr_model`: `(bool)` Controls whether to enable the OCR sub-line.
- `layout_det_res`: `(Dict[str, Union[List[numpy.ndarray], List[float]]])` Output results of the layout detection sub-module. Only exists when `use_layout_detection=True`.
- `input_path`: `(Union[str, None])` The image path accepted by the layout detection module. When the input is a `numpy.ndarray`, it is saved as `None`.
- `page_index`: `(Union[int, None])` If the input is a PDF file, it indicates which page of the PDF is currently being processed; otherwise, it is `None`.
- `boxes`: `(List[Dict])` A list of detected layout seal region boxes, with each element in the list containing the following fields:
- `cls_id`: `(int)` The class ID of the detected box.
- `score`: `(float)` The confidence score of the detected box.
- `coordinate`: `(List[float])` The coordinates of the four vertices of the detected box, in the order of x1, y1, x2, y2, representing the x-coordinate of the top-left corner, the y-coordinate of the top-left corner, the x-coordinate of the bottom-right corner, and the y-coordinate of the bottom-right corner.
- `doc_preprocessor_res`: `(Dict[str, Union[str, Dict[str, bool], int]])` Output results of the document preprocessing sub-line. Only exists when `use_doc_preprocessor=True`.
- `input_path`: `(Union[str, None])` The image path accepted by the preprocessing sub-line. When the input is a `numpy.ndarray`, it is saved as `None`.
- `model_settings`: `(Dict)` Model configuration parameters for the preprocessing sub-line.
- `use_doc_orientation_classify`: `(bool)` Controls whether to enable document orientation classification.
- `use_doc_unwarping`: `(bool)` Controls whether to enable document unwarping.
- `angle`: `(int)` The predicted result of document orientation classification. When enabled, the values are [0,1,2,3], corresponding to [0°,90°,180°,270°]; when disabled, it is -1.
- `dt_polys`: `(List[numpy.ndarray])` A list of polygon boxes for text detection. Each detection box is represented by a numpy array of 4 vertex coordinates, with the array shape being (4, 2) and data type being int16.
- `dt_scores`: `(List[float])` A list of confidence scores for text detection boxes.
- `text_det_params`: `(Dict[str, Dict[str, int, float]])` Configuration parameters for the text detection module.
- `limit_side_len`: `(int)` The side length limit value for image preprocessing.
- `limit_type`: `(str)` The processing method for side length limits.
- `thresh`: `(float)` The confidence threshold for text pixel classification.
- `box_thresh`: `(float)` The confidence threshold for text detection boxes.
- `unclip_ratio`: `(float)` The expansion ratio for text detection boxes.
- `text_type`: `(str)` The type of text detection, currently fixed as "general".
- `text_rec_score_thresh`: `(float)` The filtering threshold for text recognition results.
- `rec_texts`: `(List[str])` A list of text recognition results, containing only texts with confidence scores above `text_rec_score_thresh`.
- `rec_scores`: `(List[float])` A list of confidence scores for text recognition, filtered by `text_rec_score_thresh`.
- `rec_polys`: `(List[numpy.ndarray])` A list of text detection boxes filtered by confidence score, in the same format as `dt_polys`.
- `rec_boxes`: `(numpy.ndarray)` An array of rectangular bounding boxes for detection boxes, with shape (n, 4) and dtype int16. Each row represents the [x_min, y_min, x_max, y_max] coordinates of a rectangular box, where (x_min, y_min) is the top-left corner and (x_max, y_max) is the bottom-right corner.
- Calling the `save_to_json()` method will save the above content to the specified `save_path`. If specified as a directory, the saved path will be `save_path/{your_img_basename}.json`; if specified as a file, it will be saved directly to that file. Since JSON files do not support saving numpy arrays, the `numpy.array` types will be converted to lists.
- Calling the `save_to_img()` method will save the visualization results to the specified `save_path`. If specified as a directory, the saved path will be `save_path/{your_img_basename}_ocr_res_img.{your_img_extension}`; if specified as a file, it will be saved directly to that file. (The pipeline usually contains many result images, it is not recommended to specify a specific file path directly, otherwise multiple images will be overwritten, leaving only the last image)
- Calling the `save_to_html()` method will save the above content to the specified `save_path`. If specified as a directory, the saved path will be `save_path/{your_img_basename}.html`; if specified as a file, it will be saved directly to that file. In the General Table Recognition v2 Pipeline, the HTML form of the table in the image will be written to the specified HTML file.
- Calling the `save_to_xlsx()` method will save the above content to the specified `save_path`. If specified as a directory, the saved path will be `save_path/{your_img_basename}.xlsx`; if specified as a file, it will be saved directly to that file. In the General Table Recognition v2 Pipeline, the Excel form of the table in the image will be written to the specified XLSX file.
* Additionally, it is also possible to obtain the visualization image with results and prediction results through attributes, as follows:
The General Table Recognition v2 Pipeline includes mandatory modules such as table structure recognition, table classification, table cell localization, text detection, and text recognition, as well as optional modules like layout area detection, document image orientation classification, and text image correction.
If you prioritize model accuracy, choose a model with higher accuracy; if you care more about inference speed, choose a model with faster inference speed; if you are concerned about model storage size, choose a model with a smaller storage size.
