PaddleOCR MCP Server

# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import warnings from .._utils.cli import ( add_simple_inference_args, get_subcommand_args, perform_simple_inference, str2bool, ) from .base import PaddleXPipelineWrapper, PipelineCLISubcommandExecutor from .utils import create_config_from_structure _SUPPORTED_OCR_VERSIONS = ["PP-OCRv3", "PP-OCRv4", "PP-OCRv5"] class PPStructureV3(PaddleXPipelineWrapper): def __init__( self, layout_detection_model_name=None, layout_detection_model_dir=None, layout_threshold=None, layout_nms=None, layout_unclip_ratio=None, layout_merge_bboxes_mode=None, chart_recognition_model_name=None, chart_recognition_model_dir=None, chart_recognition_batch_size=None, region_detection_model_name=None, region_detection_model_dir=None, doc_orientation_classify_model_name=None, doc_orientation_classify_model_dir=None, doc_unwarping_model_name=None, doc_unwarping_model_dir=None, text_detection_model_name=None, text_detection_model_dir=None, text_det_limit_side_len=None, text_det_limit_type=None, text_det_thresh=None, text_det_box_thresh=None, text_det_unclip_ratio=None, textline_orientation_model_name=None, textline_orientation_model_dir=None, textline_orientation_batch_size=None, text_recognition_model_name=None, text_recognition_model_dir=None, text_recognition_batch_size=None, text_rec_score_thresh=None, table_classification_model_name=None, table_classification_model_dir=None, wired_table_structure_recognition_model_name=None, wired_table_structure_recognition_model_dir=None, wireless_table_structure_recognition_model_name=None, wireless_table_structure_recognition_model_dir=None, wired_table_cells_detection_model_name=None, wired_table_cells_detection_model_dir=None, wireless_table_cells_detection_model_name=None, wireless_table_cells_detection_model_dir=None, table_orientation_classify_model_name=None, table_orientation_classify_model_dir=None, seal_text_detection_model_name=None, seal_text_detection_model_dir=None, seal_det_limit_side_len=None, seal_det_limit_type=None, seal_det_thresh=None, seal_det_box_thresh=None, seal_det_unclip_ratio=None, seal_text_recognition_model_name=None, seal_text_recognition_model_dir=None, seal_text_recognition_batch_size=None, seal_rec_score_thresh=None, formula_recognition_model_name=None, formula_recognition_model_dir=None, formula_recognition_batch_size=None, use_doc_orientation_classify=None, use_doc_unwarping=None, use_textline_orientation=None, use_seal_recognition=None, use_table_recognition=None, use_formula_recognition=None, use_chart_recognition=None, use_region_detection=None, lang=None, ocr_version=None, **kwargs, ): if ocr_version is not None and ocr_version not in _SUPPORTED_OCR_VERSIONS: raise ValueError( f"Invalid OCR version: {ocr_version}. Supported values are {_SUPPORTED_OCR_VERSIONS}." ) if all( map( lambda p: p is None, ( text_detection_model_name, text_detection_model_dir, text_recognition_model_name, text_recognition_model_dir, ), ) ): if lang is not None or ocr_version is not None: det_model_name, rec_model_name = self._get_ocr_model_names( lang, ocr_version ) if det_model_name is None or rec_model_name is None: raise ValueError( f"No models are available for the language {repr(lang)} and OCR version {repr(ocr_version)}." ) text_detection_model_name = det_model_name text_recognition_model_name = rec_model_name else: if lang is not None or ocr_version is not None: warnings.warn( "`lang` and `ocr_version` will be ignored when model names or model directories are not `None`.", stacklevel=2, ) params = locals().copy() params["text_detection_model_name"] = text_detection_model_name params["text_recognition_model_name"] = text_recognition_model_name params.pop("self") params.pop("kwargs") self._params = params super().__init__(**kwargs) @property def _paddlex_pipeline_name(self): return "PP-StructureV3" def predict_iter( self, input, *, use_doc_orientation_classify=None, use_doc_unwarping=None, use_textline_orientation=None, use_seal_recognition=None, use_table_recognition=None, use_formula_recognition=None, use_chart_recognition=None, use_region_detection=None, layout_threshold=None, layout_nms=None, layout_unclip_ratio=None, layout_merge_bboxes_mode=None, text_det_limit_side_len=None, text_det_limit_type=None, text_det_thresh=None, text_det_box_thresh=None, text_det_unclip_ratio=None, text_rec_score_thresh=None, seal_det_limit_side_len=None, seal_det_limit_type=None, seal_det_thresh=None, seal_det_box_thresh=None, seal_det_unclip_ratio=None, seal_rec_score_thresh=None, use_wired_table_cells_trans_to_html=False, use_wireless_table_cells_trans_to_html=False, use_table_orientation_classify=True, use_ocr_results_with_table_cells=True, use_e2e_wired_table_rec_model=False, use_e2e_wireless_table_rec_model=True, **kwargs, ): return self.paddlex_pipeline.predict( input, use_doc_orientation_classify=use_doc_orientation_classify, use_doc_unwarping=use_doc_unwarping, use_textline_orientation=use_textline_orientation, use_seal_recognition=use_seal_recognition, use_table_recognition=use_table_recognition, use_formula_recognition=use_formula_recognition, use_chart_recognition=use_chart_recognition, use_region_detection=use_region_detection, layout_threshold=layout_threshold, layout_nms=layout_nms, layout_unclip_ratio=layout_unclip_ratio, layout_merge_bboxes_mode=layout_merge_bboxes_mode, text_det_limit_side_len=text_det_limit_side_len, text_det_limit_type=text_det_limit_type, text_det_thresh=text_det_thresh, text_det_box_thresh=text_det_box_thresh, text_det_unclip_ratio=text_det_unclip_ratio, text_rec_score_thresh=text_rec_score_thresh, seal_det_limit_side_len=seal_det_limit_side_len, seal_det_limit_type=seal_det_limit_type, seal_det_thresh=seal_det_thresh, seal_det_box_thresh=seal_det_box_thresh, seal_det_unclip_ratio=seal_det_unclip_ratio, seal_rec_score_thresh=seal_rec_score_thresh, use_wired_table_cells_trans_to_html=use_wired_table_cells_trans_to_html, use_wireless_table_cells_trans_to_html=use_wireless_table_cells_trans_to_html, use_table_orientation_classify=use_table_orientation_classify, use_ocr_results_with_table_cells=use_ocr_results_with_table_cells, use_e2e_wired_table_rec_model=use_e2e_wired_table_rec_model, use_e2e_wireless_table_rec_model=use_e2e_wireless_table_rec_model, **kwargs, ) def predict( self, input, *, use_doc_orientation_classify=None, use_doc_unwarping=None, use_textline_orientation=None, use_seal_recognition=None, use_table_recognition=None, use_formula_recognition=None, use_chart_recognition=None, use_region_detection=None, layout_threshold=None, layout_nms=None, layout_unclip_ratio=None, layout_merge_bboxes_mode=None, text_det_limit_side_len=None, text_det_limit_type=None, text_det_thresh=None, text_det_box_thresh=None, text_det_unclip_ratio=None, text_rec_score_thresh=None, seal_det_limit_side_len=None, seal_det_limit_type=None, seal_det_thresh=None, seal_det_box_thresh=None, seal_det_unclip_ratio=None, seal_rec_score_thresh=None, use_wired_table_cells_trans_to_html=False, use_wireless_table_cells_trans_to_html=False, use_table_orientation_classify=True, use_ocr_results_with_table_cells=True, use_e2e_wired_table_rec_model=False, use_e2e_wireless_table_rec_model=True, **kwargs, ): return list( self.predict_iter( input, use_doc_orientation_classify=use_doc_orientation_classify, use_doc_unwarping=use_doc_unwarping, use_textline_orientation=use_textline_orientation, use_seal_recognition=use_seal_recognition, use_table_recognition=use_table_recognition, use_formula_recognition=use_formula_recognition, use_chart_recognition=use_chart_recognition, use_region_detection=use_region_detection, layout_threshold=layout_threshold, layout_nms=layout_nms, layout_unclip_ratio=layout_unclip_ratio, layout_merge_bboxes_mode=layout_merge_bboxes_mode, text_det_limit_side_len=text_det_limit_side_len, text_det_limit_type=text_det_limit_type, text_det_thresh=text_det_thresh, text_det_box_thresh=text_det_box_thresh, text_det_unclip_ratio=text_det_unclip_ratio, text_rec_score_thresh=text_rec_score_thresh, seal_det_limit_side_len=seal_det_limit_side_len, seal_det_limit_type=seal_det_limit_type, seal_det_thresh=seal_det_thresh, seal_det_box_thresh=seal_det_box_thresh, seal_det_unclip_ratio=seal_det_unclip_ratio, seal_rec_score_thresh=seal_rec_score_thresh, use_wired_table_cells_trans_to_html=use_wired_table_cells_trans_to_html, use_wireless_table_cells_trans_to_html=use_wireless_table_cells_trans_to_html, use_table_orientation_classify=use_table_orientation_classify, use_ocr_results_with_table_cells=use_ocr_results_with_table_cells, use_e2e_wired_table_rec_model=use_e2e_wired_table_rec_model, use_e2e_wireless_table_rec_model=use_e2e_wireless_table_rec_model, **kwargs, ) ) def concatenate_markdown_pages(self, markdown_list): return self.paddlex_pipeline.concatenate_markdown_pages(markdown_list) @classmethod def get_cli_subcommand_executor(cls): return PPStructureV3CLISubcommandExecutor() def _get_paddlex_config_overrides(self): STRUCTURE = { "SubPipelines.DocPreprocessor.use_doc_orientation_classify": self._params[ "use_doc_orientation_classify" ], "SubPipelines.DocPreprocessor.use_doc_unwarping": self._params[ "use_doc_unwarping" ], "use_doc_preprocessor": self._params["use_doc_orientation_classify"] or self._params["use_doc_unwarping"], "SubPipelines.GeneralOCR.use_textline_orientation": self._params[ "use_textline_orientation" ], "use_seal_recognition": self._params["use_seal_recognition"], "use_table_recognition": self._params["use_table_recognition"], "use_formula_recognition": self._params["use_formula_recognition"], "use_chart_recognition": self._params["use_chart_recognition"], "use_region_detection": self._params["use_region_detection"], "SubModules.LayoutDetection.model_name": self._params[ "layout_detection_model_name" ], "SubModules.LayoutDetection.model_dir": self._params[ "layout_detection_model_dir" ], "SubModules.LayoutDetection.threshold": self._params["layout_threshold"], "SubModules.LayoutDetection.layout_nms": self._params["layout_nms"], "SubModules.LayoutDetection.layout_unclip_ratio": self._params[ "layout_unclip_ratio" ], "SubModules.LayoutDetection.layout_merge_bboxes_mode": self._params[ "layout_merge_bboxes_mode" ], "SubModules.ChartRecognition.model_name": self._params[ "chart_recognition_model_name" ], "SubModules.ChartRecognition.model_dir": self._params[ "chart_recognition_model_dir" ], "SubModules.ChartRecognition.batch_size": self._params[ "chart_recognition_batch_size" ], "SubModules.RegionDetection.model_name": self._params[ "region_detection_model_name" ], "SubModules.RegionDetection.model_dir": self._params[ "region_detection_model_dir" ], "SubPipelines.DocPreprocessor.SubModules.DocOrientationClassify.model_name": self._params[ "doc_orientation_classify_model_name" ], "SubPipelines.DocPreprocessor.SubModules.DocOrientationClassify.model_dir": self._params[ "doc_orientation_classify_model_dir" ], "SubPipelines.DocPreprocessor.SubModules.DocUnwarping.model_name": self._params[ "doc_unwarping_model_name" ], "SubPipelines.DocPreprocessor.SubModules.DocUnwarping.model_dir": self._params[ "doc_unwarping_model_dir" ], "SubPipelines.GeneralOCR.SubModules.TextDetection.model_name": self._params[ "text_detection_model_name" ], "SubPipelines.GeneralOCR.SubModules.TextDetection.model_dir": self._params[ "text_detection_model_dir" ], "SubPipelines.GeneralOCR.SubModules.TextDetection.limit_side_len": self._params[ "text_det_limit_side_len" ], "SubPipelines.GeneralOCR.SubModules.TextDetection.limit_type": self._params[ "text_det_limit_type" ], "SubPipelines.GeneralOCR.SubModules.TextDetection.thresh": self._params[ "text_det_thresh" ], "SubPipelines.GeneralOCR.SubModules.TextDetection.box_thresh": self._params[ "text_det_box_thresh" ], "SubPipelines.GeneralOCR.SubModules.TextDetection.unclip_ratio": self._params[ "text_det_unclip_ratio" ], "SubPipelines.GeneralOCR.SubModules.TextLineOrientation.model_name": self._params[ "textline_orientation_model_name" ], "SubPipelines.GeneralOCR.SubModules.TextLineOrientation.model_dir": self._params[ "textline_orientation_model_dir" ], "SubPipelines.GeneralOCR.SubModules.TextLineOrientation.batch_size": self._params[ "textline_orientation_batch_size" ], "SubPipelines.GeneralOCR.SubModules.TextRecognition.model_name": self._params[ "text_recognition_model_name" ], "SubPipelines.GeneralOCR.SubModules.TextRecognition.model_dir": self._params[ "text_recognition_model_dir" ], "SubPipelines.GeneralOCR.SubModules.TextRecognition.batch_size": self._params[ "text_recognition_batch_size" ], "SubPipelines.GeneralOCR.SubModules.TextRecognition.score_thresh": self._params[ "text_rec_score_thresh" ], "SubPipelines.TableRecognition.SubModules.TableClassification.model_name": self._params[ "table_classification_model_name" ], "SubPipelines.TableRecognition.SubModules.TableClassification.model_dir": self._params[ "table_classification_model_dir" ], "SubPipelines.TableRecognition.SubModules.WiredTableStructureRecognition.model_name": self._params[ "wired_table_structure_recognition_model_name" ], "SubPipelines.TableRecognition.SubModules.WiredTableStructureRecognition.model_dir": self._params[ "wired_table_structure_recognition_model_dir" ], "SubPipelines.TableRecognition.SubModules.WirelessTableStructureRecognition.model_name": self._params[ "wireless_table_structure_recognition_model_name" ], "SubPipelines.TableRecognition.SubModules.WirelessTableStructureRecognition.model_dir": self._params[ "wireless_table_structure_recognition_model_dir" ], "SubPipelines.TableRecognition.SubModules.WiredTableCellsDetection.model_name": self._params[ "wired_table_cells_detection_model_name" ], "SubPipelines.TableRecognition.SubModules.WiredTableCellsDetection.model_dir": self._params[ "wired_table_cells_detection_model_dir" ], "SubPipelines.TableRecognition.SubModules.WirelessTableCellsDetection.model_name": self._params[ "wireless_table_cells_detection_model_name" ], "SubPipelines.TableRecognition.SubModules.WirelessTableCellsDetection.model_dir": self._params[ "wireless_table_cells_detection_model_dir" ], "SubPipelines.TableRecognition.SubModules.TableOrientationClassify.model_name": self._params[ "table_orientation_classify_model_name" ], "SubPipelines.TableRecognition.SubModules.TableOrientationClassify.model_dir": self._params[ "table_orientation_classify_model_dir" ], "SubPipelines.TableRecognition.SubPipelines.GeneralOCR.SubModules.TextDetection.model_name": self._params[ "text_detection_model_name" ], "SubPipelines.TableRecognition.SubPipelines.GeneralOCR.SubModules.TextDetection.model_dir": self._params[ "text_detection_model_dir" ], "SubPipelines.TableRecognition.SubPipelines.GeneralOCR.SubModules.TextDetection.limit_side_len": self._params[ "text_det_limit_side_len" ], "SubPipelines.TableRecognition.SubPipelines.GeneralOCR.SubModules.TextDetection.limit_type": self._params[ "text_det_limit_type" ], "SubPipelines.TableRecognition.SubPipelines.GeneralOCR.SubModules.TextDetection.thresh": self._params[ "text_det_thresh" ], "SubPipelines.TableRecognition.SubPipelines.GeneralOCR.SubModules.TextDetection.box_thresh": self._params[ "text_det_box_thresh" ], "SubPipelines.TableRecognition.SubPipelines.GeneralOCR.SubModules.TextDetection.unclip_ratio": self._params[ "text_det_unclip_ratio" ], "SubPipelines.TableRecognition.SubPipelines.GeneralOCR.SubModules.TextLineOrientation.model_name": self._params[ "textline_orientation_model_name" ], "SubPipelines.TableRecognition.SubPipelines.GeneralOCR.SubModules.TextLineOrientation.model_dir": self._params[ "textline_orientation_model_dir" ], "SubPipelines.TableRecognition.SubPipelines.GeneralOCR.SubModules.TextLineOrientation.batch_size": self._params[ "textline_orientation_batch_size" ], "SubPipelines.TableRecognition.SubPipelines.GeneralOCR.SubModules.TextRecognition.model_name": self._params[ "text_recognition_model_name" ], "SubPipelines.TableRecognition.SubPipelines.GeneralOCR.SubModules.TextRecognition.model_dir": self._params[ "text_recognition_model_dir" ], "SubPipelines.TableRecognition.SubPipelines.GeneralOCR.SubModules.TextRecognition.batch_size": self._params[ "text_recognition_batch_size" ], "SubPipelines.TableRecognition.SubPipelines.GeneralOCR.SubModules.TextRecognition.score_thresh": self._params[ "text_rec_score_thresh" ], "SubPipelines.SealRecognition.SubPipelines.SealOCR.SubModules.TextDetection.model_name": self._params[ "seal_text_detection_model_name" ], "SubPipelines.SealRecognition.SubPipelines.SealOCR.SubModules.TextDetection.model_dir": self._params[ "seal_text_detection_model_dir" ], "SubPipelines.SealRecognition.SubPipelines.SealOCR.SubModules.TextDetection.limit_side_len": self._params[ "text_det_limit_side_len" ], "SubPipelines.SealRecognition.SubPipelines.SealOCR.SubModules.TextDetection.limit_type": self._params[ "seal_det_limit_type" ], "SubPipelines.SealRecognition.SubPipelines.SealOCR.SubModules.TextDetection.thresh": self._params[ "seal_det_thresh" ], "SubPipelines.SealRecognition.SubPipelines.SealOCR.SubModules.TextDetection.box_thresh": self._params[ "seal_det_box_thresh" ], "SubPipelines.SealRecognition.SubPipelines.SealOCR.SubModules.TextDetection.unclip_ratio": self._params[ "seal_det_unclip_ratio" ], "SubPipelines.SealRecognition.SubPipelines.SealOCR.SubModules.TextRecognition.model_name": self._params[ "seal_text_recognition_model_name" ], "SubPipelines.SealRecognition.SubPipelines.SealOCR.SubModules.TextRecognition.model_dir": self._params[ "seal_text_recognition_model_dir" ], "SubPipelines.SealRecognition.SubPipelines.SealOCR.SubModules.TextRecognition.batch_size": self._params[ "seal_text_recognition_batch_size" ], "SubPipelines.FormulaRecognition.SubModules.FormulaRecognition.model_name": self._params[ "formula_recognition_model_name" ], "SubPipelines.FormulaRecognition.SubModules.FormulaRecognition.model_dir": self._params[ "formula_recognition_model_dir" ], "SubPipelines.FormulaRecognition.SubModules.FormulaRecognition.batch_size": self._params[ "formula_recognition_batch_size" ], } return create_config_from_structure(STRUCTURE) def _get_ocr_model_names(self, lang, ppocr_version): LATIN_LANGS = [ "af", "az", "bs", "cs", "cy", "da", "de", "es", "et", "fr", "ga", "hr", "hu", "id", "is", "it", "ku", "la", "lt", "lv", "mi", "ms", "mt", "nl", "no", "oc", "pi", "pl", "pt", "ro", "rs_latin", "sk", "sl", "sq", "sv", "sw", "tl", "tr", "uz", "vi", "french", "german", ] ARABIC_LANGS = ["ar", "fa", "ug", "ur"] ESLAV_LANGS = ["ru", "be", "uk"] CYRILLIC_LANGS = [ "ru", "rs_cyrillic", "be", "bg", "uk", "mn", "abq", "ady", "kbd", "ava", "dar", "inh", "che", "lbe", "lez", "tab", ] DEVANAGARI_LANGS = [ "hi", "mr", "ne", "bh", "mai", "ang", "bho", "mah", "sck", "new", "gom", "sa", "bgc", ] SPECIFIC_LANGS = [ "ch", "en", "korean", "japan", "chinese_cht", "te", "ka", "ta", ] if lang is None: lang = "ch" if ppocr_version is None: if ( lang in ["ch", "chinese_cht", "en", "japan", "korean", "th", "el"] + LATIN_LANGS + ESLAV_LANGS ): ppocr_version = "PP-OCRv5" elif lang in ( LATIN_LANGS + ARABIC_LANGS + CYRILLIC_LANGS + DEVANAGARI_LANGS + SPECIFIC_LANGS ): ppocr_version = "PP-OCRv3" else: # Unknown language specified return None, None if ppocr_version == "PP-OCRv5": rec_lang, rec_model_name = None, None if lang in ("ch", "chinese_cht", "en", "japan"): rec_model_name = "PP-OCRv5_server_rec" elif lang in LATIN_LANGS: rec_lang = "latin" elif lang in ESLAV_LANGS: rec_lang = "eslav" elif lang == "korean": rec_lang = "korean" elif lang == "th": rec_lang = "th" elif lang == "el": rec_lang = "el" if rec_lang is not None: rec_model_name = f"{rec_lang}_PP-OCRv5_mobile_rec" return "PP-OCRv5_server_det", rec_model_name elif ppocr_version == "PP-OCRv4": if lang == "ch": return "PP-OCRv4_mobile_det", "PP-OCRv4_mobile_rec" elif lang == "en": return "PP-OCRv4_mobile_det", "en_PP-OCRv4_mobile_rec" else: return None, None else: # PP-OCRv3 rec_lang = None if lang in LATIN_LANGS: rec_lang = "latin" elif lang in ARABIC_LANGS: rec_lang = "arabic" elif lang in CYRILLIC_LANGS: rec_lang = "cyrillic" elif lang in DEVANAGARI_LANGS: rec_lang = "devanagari" else: if lang in SPECIFIC_LANGS: rec_lang = lang rec_model_name = None if rec_lang == "ch": rec_model_name = "PP-OCRv3_mobile_rec" elif rec_lang is not None: rec_model_name = f"{rec_lang}_PP-OCRv3_mobile_rec" return "PP-OCRv3_mobile_det", rec_model_name class PPStructureV3CLISubcommandExecutor(PipelineCLISubcommandExecutor): @property def subparser_name(self): return "pp_structurev3" def _update_subparser(self, subparser): add_simple_inference_args(subparser) subparser.add_argument( "--layout_detection_model_name", type=str, help="Name of the layout detection model.", ) subparser.add_argument( "--layout_detection_model_dir", type=str, help="Path to the layout detection model directory.", ) subparser.add_argument( "--layout_threshold", type=float, help="Score threshold for the layout detection model.", ) subparser.add_argument( "--layout_nms", type=str2bool, help="Whether to use NMS in layout detection.", ) subparser.add_argument( "--layout_unclip_ratio", type=float, help="Expansion coefficient for layout detection.", ) subparser.add_argument( "--layout_merge_bboxes_mode", type=str, help="Overlapping box filtering method.", ) subparser.add_argument( "--chart_recognition_model_name", type=str, help="Name of the chart recognition model.", ) subparser.add_argument( "--chart_recognition_model_dir", type=str, help="Path to the chart recognition model directory.", ) subparser.add_argument( "--chart_recognition_batch_size", type=int, help="Batch size for the chart recognition model.", ) subparser.add_argument( "--region_detection_model_name", type=str, help="Name of the region detection model.", ) subparser.add_argument( "--region_detection_model_dir", type=str, help="Path to the region detection model directory.", ) subparser.add_argument( "--doc_orientation_classify_model_name", type=str, help="Name of the document image orientation classification model.", ) subparser.add_argument( "--doc_orientation_classify_model_dir", type=str, help="Path to the document image orientation classification model directory.", ) subparser.add_argument( "--doc_unwarping_model_name", type=str, help="Name of the text image unwarping model.", ) subparser.add_argument( "--doc_unwarping_model_dir", type=str, help="Path to the image unwarping model directory.", ) subparser.add_argument( "--text_detection_model_name", type=str, help="Name of the text detection model.", ) subparser.add_argument( "--text_detection_model_dir", type=str, help="Path to the text detection model directory.", ) subparser.add_argument( "--text_det_limit_side_len", type=int, help="This sets a limit on the side length of the input image for the text detection model.", ) subparser.add_argument( "--text_det_limit_type", type=str, help="This determines how the side length limit is applied to the input image before feeding it into the text deteciton model.", ) subparser.add_argument( "--text_det_thresh", type=float, help="Detection pixel threshold for the text detection model. Pixels with scores greater than this threshold in the output probability map are considered text pixels.", ) subparser.add_argument( "--text_det_box_thresh", type=float, help="Detection box threshold for the text detection model. A detection result is considered a text region if the average score of all pixels within the border of the result is greater than this threshold.", ) subparser.add_argument( "--text_det_unclip_ratio", type=float, help="Text detection expansion coefficient, which expands the text region using this method. The larger the value, the larger the expansion area.", ) subparser.add_argument( "--textline_orientation_model_name", type=str, help="Name of the text line orientation classification model.", ) subparser.add_argument( "--textline_orientation_model_dir", type=str, help="Path to the text line orientation classification directory.", ) subparser.add_argument( "--textline_orientation_batch_size", type=int, help="Batch size for the text line orientation classification model.", ) subparser.add_argument( "--text_recognition_model_name", type=str, help="Name of the text recognition model.", ) subparser.add_argument( "--text_recognition_model_dir", type=str, help="Path to the text recognition model directory.", ) subparser.add_argument( "--text_recognition_batch_size", type=int, help="Batch size for the text recognition model.", ) subparser.add_argument( "--text_rec_score_thresh", type=float, help="Text recognition threshold used in general OCR. Text results with scores greater than this threshold are retained.", ) subparser.add_argument( "--table_classification_model_name", type=str, help="Name of the table classification model.", ) subparser.add_argument( "--table_classification_model_dir", type=str, help="Path to the table classification model directory.", ) subparser.add_argument( "--wired_table_structure_recognition_model_name", type=str, help="Name of the wired table structure recognition model.", ) subparser.add_argument( "--wired_table_structure_recognition_model_dir", type=str, help="Path to the wired table structure recognition model directory.", ) subparser.add_argument( "--wireless_table_structure_recognition_model_name", type=str, help="Name of the wireless table structure recognition model.", ) subparser.add_argument( "--wireless_table_structure_recognition_model_dir", type=str, help="Path to the wired table structure recognition model directory.", ) subparser.add_argument( "--wired_table_cells_detection_model_name", type=str, help="Name of the wired table cells detection model.", ) subparser.add_argument( "--wired_table_cells_detection_model_dir", type=str, help="Path to the wired table cells detection model directory.", ) subparser.add_argument( "--wireless_table_cells_detection_model_name", type=str, help="Name of the wireless table cells detection model.", ) subparser.add_argument( "--wireless_table_cells_detection_model_dir", type=str, help="Path to the wireless table cells detection model directory.", ) subparser.add_argument( "--seal_text_detection_model_name", type=str, help="Name of the seal text detection model.", ) subparser.add_argument( "--seal_text_detection_model_dir", type=str, help="Path to the seal text detection model directory.", ) subparser.add_argument( "--seal_det_limit_side_len", type=int, help="This sets a limit on the side length of the input image for the seal text detection model.", ) subparser.add_argument( "--seal_det_limit_type", type=str, help="This determines how the side length limit is applied to the input image before feeding it into the seal text deteciton model.", ) subparser.add_argument( "--seal_det_thresh", type=float, help="Detection pixel threshold for the seal text detection model. Pixels with scores greater than this threshold in the output probability map are considered text pixels.", ) subparser.add_argument( "--seal_det_box_thresh", type=float, help="Detection box threshold for the seal text detection model. A detection result is considered a text region if the average score of all pixels within the border of the result is greater than this threshold.", ) subparser.add_argument( "--seal_det_unclip_ratio", type=float, help="Seal text detection expansion coefficient, which expands the text region using this method. The larger the value, the larger the expansion area.", ) subparser.add_argument( "--seal_text_recognition_model_name", type=str, help="Name of the seal text recognition model.", ) subparser.add_argument( "--seal_text_recognition_model_dir", type=str, help="Path to the seal text recognition model directory.", ) subparser.add_argument( "--seal_text_recognition_batch_size", type=int, help="Batch size for the seal text recognition model.", ) subparser.add_argument( "--seal_rec_score_thresh", type=float, help="Seal text recognition threshold. Text results with scores greater than this threshold are retained.", ) subparser.add_argument( "--formula_recognition_model_name", type=str, help="Name of the formula recognition model.", ) subparser.add_argument( "--formula_recognition_model_dir", type=str, help="Path to the formula recognition model directory.", ) subparser.add_argument( "--formula_recognition_batch_size", type=int, help="Batch size for the formula recognition model.", ) subparser.add_argument( "--use_doc_orientation_classify", type=str2bool, help="Whether to use document image orientation classification.", ) subparser.add_argument( "--use_doc_unwarping", type=str2bool, help="Whether to use text image unwarping.", ) subparser.add_argument( "--use_textline_orientation", type=str2bool, help="Whether to use text line orientation classification.", ) subparser.add_argument( "--use_seal_recognition", type=str2bool, help="Whether to use seal recognition.", ) subparser.add_argument( "--use_table_recognition", type=str2bool, help="Whether to use table recognition.", ) subparser.add_argument( "--use_formula_recognition", type=str2bool, help="Whether to use formula recognition.", ) subparser.add_argument( "--use_chart_recognition", type=str2bool, help="Whether to use chart recognition.", ) subparser.add_argument( "--use_region_detection", type=str2bool, help="Whether to use region detection.", ) def execute_with_args(self, args): params = get_subcommand_args(args) perform_simple_inference( PPStructureV3, params, )