smart-pdf-for-business 1.0.0

A Python library for business PDF related content analysis

Smart PDF for Business: Extract Text, Headings, and Signatures from PDFs

Smart PDF for Business is a Python library for structured PDF content analysis, built on top of spaCy-layout. Using spaCy-layout’s page, block, and layout-aware text extraction capabilities, the library adds higher-level features for business workflows and document automation. Smart PDF for Business provides a unified interface to extract, search, and analyze PDF documents with structure-aware intelligence:

• Load PDFs from files, folders, or raw bytes.
• Extract headings, body text, sections, and layout-aware content.
• Search text using keywords or semantic similarity powered by SentenceTransformers.
• Detect handwritten or scanned signatures with bounding boxes, pages, and optional cropped images.
• Export results as plain text, Markdown, CSV, Excel, or JSON.
• Process multiple PDFs at once with batch utilities.

📝 Usage

⚠️ This package requires Python 3.10 or above.

pip install smart_pdf_for_business

After initialising a PDFDoc object using one of the factory methods, you can call its built-in functions. Most methods create a new PDFDoc object containing the result. This allows you to utilise the library's output capabilities.

from smart_pdf_for_business import PDFDoc

# Load a PDF
pdf = PDFDoc.from_file("example.pdf")

# Perform semantic search
clauses = pdf.search_by_meaning("termination clause", chunk_by="semantic")

# Export results
clauses.to_csv("output.csv")
clauses.to_excel("output.xlsx")

Alternatively, you can use as_tuple=True to return the result as a tuple.

from smart_pdf_for_business import PDFDoc

# Load a PDF
pdf = PDFDoc.from_file("example.pdf")

# Perform semantic search
clauses = pdf.search_by_meaning("termination clause", chunk_by="semantic", as_tuple=True)

# Print result as tuple
print(clauses)

📚 API

class PDFDoc

Attribute	Description
data: bytes	Raw byte content of the PDF document.
path: Optional[Path]	File system path to the PDF file, if loaded from disk.
name: Optional[str]	Name of the PDF document (usually the filename).
spacy_doc: Optional[Doc]	Processed spaCy Doc object, containing text, layout, and markdown.
sections: List[Tuple[str, str]]	List of (title, body) tuples extracted from the document.
weighted_sections: List[Tuple[Tuple[str, str], float]]	List of sections paired with a semantic match weight.
signatures: List[Signature]	Detected signatures stored as Signature objects.

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Methods	Description	Parameter
from_file	Creates a PDFDoc from a PDF file. Return: PDFDoc	path: str | Path - Path to the PDF file
from_folder	Loads all PDFs from a folder (optionally recursively). Return: list[PDFDoc]	folder_path: str | Path - Folder containing PDFs recursive: bool = True - Include subfolders
from_bytes	Creates a PDFDoc from raw PDF byte content. Return: PDFDoc	data: bytes - PDF bytes name: Optional[str] = None - Name assigned to document
from_byte_list	Creates multiple PDFDoc objects from PDF byte streams. Return: list[PDFDoc]	byte_list: List[bytes] - List of PDF byte streams names: Optional[List[str]] = None - Corresponding PDF names
search_signature	Detects signatures near keyword regions and optionally saves cropped images. Updates self.signatures. Return: PDFDoc	keywords: list[str] - Signature-related keywords exact=False - Forbid substring matching max_distance=70 - Max pixel distance save_folder=None - Folder to save crops min_contrast=30 - Minimum image contrast filter_stroke_density=True - Stroke-density filtering enforce_text_type: bool = True - Filter signature text using nlp
search_header	Searches section headers for keyword matches. Return: PDFDoc or list[tuple[str, str]]	keywords: list[str] - Header keywords exact: bool = True - Exact match as_tuple: bool = False - Return tuples
search_body	Searches section bodies for keyword matches. Return: PDFDoc or list[tuple[str, str]]	keywords: list[str] - Body keywords exact: bool = True - Exact match as_tuple: bool = False - Return tuples
search_by_meaning	Performs semantic search across sections, sentences, paragraphs, or words. Return: PDFDoc or list[tuple[str, float]]	query: str - Search query threshold: float = 0 - Minimum similarity chunk_by: str = "section" - Chunking strategy as_tuple: bool = False - Return raw tuples word_count: int = 5 - Words per chunk buffer_size: int = 1 - Chunk overlap chunk_text_semantically_threshold: float = 0.3 - Semantic chunk threshold max_results: int = 5 - Max results
to_text	Converts the PDF to plain text with optional signature annotations. Return: str	annotate_signatures: bool = True - Annotate signature positions
to_markdown	Converts the PDF to Markdown with optional signature annotations. Return: str	annotate_signatures: bool = True - Annotate signature positions
to_dataframe	Converts the document to a structured pandas DataFrame. Return: pandas.DataFrame	None
to_excel	Exports the document DataFrame to an Excel (.xlsx) file. Return: None	path: str | Path - Output .xlsx path sheet_name: str = "Sheet1" - Sheet name
to_csv	Exports the document DataFrame to a CSV file. Return: None	path: str | Path - Output CSV file path
to_json	Serializes the document into JSON, optionally writing to a file. Return: str (JSON) or None (if saved to file)	path: Optional[str | Path] = None - Optional save path indent: int = 2 - JSON formatting

class PDFDocBatch

Attribute	Description
pdfdocs: list[PDFDoc]	List containing all PDFDoc instances in the batch.

---

Methods	Description	Parameter
from_folder	Creates a batch from all PDFs in a folder. Return: PDFDocBatch	path: str | Path - Path to the folder containing PDFs recursive: bool = True - Whether to include subfolders
from_byte_list	Creates a batch from a list of PDF byte streams. Return: PDFDocBatch	byte_list: List[bytes] - List of PDF bytes names: Optional[List[str]] = None - Optional list of names corresponding to each PDF
from_pdfdoc_list	Creates a batch from an existing list of PDFDoc objects. Return: PDFDocBatch	pdfdoc_list: List[PDFDoc] - List of PDFDoc instances
extend	Extends the batch with another batch or list of PDFs. Return: None	other: Union[List[PDFDoc], PDFDocBatch] - Batch or list of PDFs to append
append	Appends a single PDFDoc to the batch. Return: None	pdfdoc: PDFDoc - PDFDoc instance to append
search_signature	Detects handwritten or scanned signatures associated with keyword regions for multiple PDFs. Return: PDFDocBatch	keywords: List[str] - Keywords used to locate signature-related regions exact: bool = False - Whether keyword matching must be exact max_distance: int = 70 - Maximum pixel distance between a keyword block and signature save_folder: Optional[str | Path] = None - Folder to save cropped signature images, if provided min_contrast: int = 30 - Minimum pixel contrast threshold filter_stroke_density: bool = True - Filter image regions based on stroke density enforce_text_type: bool = True - Filter signature text based on stroke density
search_header	Searches headers in every PDF for keywords. Return: PDFDocBatch	keywords: list[str] - Header keywords exact: bool = True - Exact or partial matching
search_body	Searches body text in every PDF for keywords. Return: PDFDocBatch	keywords: list[str] - Body keywords exact: bool = True - Exact or partial matching
search_by_meaning	Performs semantic search across all PDFs. Return: PDFDocBatch	query: str - Natural language query threshold: float = 0 - Min similarity score chunk_by: str = "section" - Chunking method word_count: int = 5 - Words per chunk buffer_size: int = 1 - Overlap size chunk_text_semantically_threshold: float = 0.3 - Semantic similarity threshold max_results: int = 5 - Max results per PDF
to_dataframe	Combines all PDFs in the batch into a DataFrame. PDFs with the same name overwrite each other. Return: pandas.DataFrame	None
to_excel	Exports batch data to an Excel file. Return: None	path: str | Path - Output Excel path sheet_name: str = "Sheet1" - Sheet name
to_csv	Exports batch data to a CSV file. Return: None	path: str | Path - Output CSV file path

dataclass Signature

Attribute	Description
keyword	Keyword used for search
text	OCR text of signature region
bbox	Bounding box coordinates (x1, y1, x2, y2)
page	Page number
img	Cropped image as numpy.ndarray
distance	Distance to keyword

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Contributing

Contributions are welcome! Please fork the repository and submit a pull request.

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