Whether it's regulatory texts, academic papers, or business contracts, PDF documents provide us with a convenient and stable method for information exchange. However, extracting text from PDF files is crucial for subsequent data analysis and content editing. This article will delve into the technical challenges and solutions of PDF text extraction and demonstrate the solutions ComPDFKit offers.
PDF text extraction is a technique that accurately extracts text from various PDF documents, such as books, reports, letters, etc. This technology can effectively separate text information for further processing and analysis. However, text extraction is not a simple task, since PDF documents come in different types and characteristics, necessitating different extraction methods for different types of PDF documents. ComPDFKit provides efficient text accuracy, using various algorithms, OCR, and AI. Click here to try ComPDFKit text extraction.
The Text in PDFs
PDF (Portable Document Format) is a widely used file format that preserves the original appearance of a document, irrespective of the operating system, software, or hardware. PDF files can comprise various components, such as images, text, links, and tables, which can offer a wealth of information and functionality.
Fundamentally, PDF does not represent the text as lines or words, but as individual characters drawn at specific positions on a page. It presents words, lines, and paragraphs that are easy for the human eye to understand. From a programming perspective, these constructs are less apparent, and you need to infer them from the original drawing commands. To extract the text from PDFs, we need to compose the appropriate words, sentences, lines, and paragraphs of text by reconstructing individual characters drawn and comparing the distances of those characters from others into words along with their position in the file.
Image by ComPDFKIt
PDF Types & Challenges in Text Extraction
First, all PDF files need to address issues like text reading order (right-to-left, left-to-right, top-to-bottom), difficulties in splitting text into lines, identifying multiple languages, etc. Then, there are specific problems for extracting text from different types of PDF files, all of which ComPDFKit text extraction technology has resolved (See details in the next section). In this part, you can see the PDF types and text extraction challenges.
1. Programmatically Generated PDF: These PDFs are created on computers using W3C technologies like HTML, CSS, and Javascript or other software such as Adobe Acrobat. Their text content is typically stored in the form of content streams. Such files can contain various components, such as images, text, and links, which are searchable and easy to edit. Issues in extracting text from such files include:
- Extracting text from content streams: Since these streams only indicate to the rendering engine what to draw on the screen, and because space is a non-entity, most of the time, we must infer spaces and line breaks ourselves. Hidden text, extra spaces or missing spaces, and ligatures all increase the difficulty of text extraction.
- Unsupported/unreadable characters: Some PDF documents may use uncommon or non-standard fonts or encodings that can cause text extraction tools to fail to recognize or display these characters correctly. For instance, some PDF documents might contain unreadable characters like “fo� P� �.”
2. Scanned PDFs: These files are merely collections of images stored within a PDF file. Elements within these images, such as text or links, cannot be selected or searched. Essentially, the PDF acts as a container for these images. This kind of file requires Optical Character Recognition (OCR) technology to recognize the image text and convert it into searchable and editable text. However, OCR technology can be affected by image quality, such as:
- Image shadows, noise interference, etc.: Poor quality of the scanned documents or equipment, or insufficient scanning environment lighting could lead to shadows, noise, and other interferences in the image, affecting the accuracy and quality of OCR.
- Image skew: If the scanned documents or equipment aren't positioned correctly, or if there's movement during scanning, this could result in the textual content in images being skewed, which could affect the accuracy and quality of OCR.
3. Documents Scanned with OCR: In such cases, documents are scanned, and OCR software is used to recognize the text in each image, converting it into searchable and editable text. These types of files have already undergone OCR recognition, but there may still be issues with accuracy. Any text extraction built on this may start with certain inaccuracies, such as:
- Mismatches between the text layer and the image layer, missing or incorrect text layers, incorrect text layer order, etc., all of which affect the quality and effectiveness of text extraction.
ComPDFKit Solutions
For text extraction technology, ComPDFKit offers the following two solutions that effectively address text extraction for all types of PDF files. For documents containing only text information, our non-intelligent solution can suffice. But for more complex documents and image-based ones, ComPDFKit Document AI offers higher accuracy in text extraction. To learn about the accuracy of ComPDFKit's information extraction, see this article.
1. Algorithm: X-Y Cut Recursion Projection Method
The X-Y Cut Recursion Projection Method is a top-down page segmentation technique that decomposes a document image into rectangular blocks. It employs a recursive approach by projecting along both the X and Y axes to segment a PDF into independent rectangles, facilitating the extraction of textual components. ComPDFKit utilizes this method for efficient text separation and structural organization, including rows, paragraphs, and columns, to retrieve characters, words, lines, and paragraphs from the document.
The advantage of the X-Y Cut Recursion Projection Method is its speed, making it suitable for simple, structured, non-image-based PDF documents. However, for complex, unstructured PDFs, there might be recognition errors or omissions.
2. ComPDFKit Document AI
Document AI is an intelligent text extraction solution supporting all types of PDF files, including image-based. It uses artificial intelligence-based methods for document recognition and analysis to extract textual information from PDF documents (as well as images, tables, etc.).
- PDF Recognition and Analysis: This involves using deep learning models to recognize and analyze PDF documents, extracting elements like text, images, and tables while retaining their position, size, style, etc. ComPDFKit owns well-trained AI models to accomplish this process.
- Image Pre-processing: This process involves improving the quality and clarity of low-quality images in PDF documents, enhancing subsequent recognition and analysis. ComPDFKit employs multiple image processing techniques, such as image sharpening enhancement, noise reduction, document trimming and straightening, and stamp detection.
- OCR (Optical Character Recognition): OCR technology has a wide range of application scenarios such as license plate recognition, bank card information extraction, identity document (ID card) information recognition, train ticket information detection, etc. ComPDFKit supports recognition in dozens of languages. With extensively trained model zoo, it can accurately detect and recognize text in documents and analyze document structure.
Data Export
Data export refers to the function of exporting the textual information extracted by ComPDFKit in various file formats, assisting in subsequent editing, analysis, presentation, etc. ComPDFKit supports the following data format types and their corresponding document purposes:
- JSON (JavaScript Object Notation): A lightweight data-interchange format that can organize and store text information as key-value pairs, modifiable or analyzable, representing complex data structures in concise text, and facilitating interaction with various programming languages.
- CSV (Comma-Separated Values): It is a text file format that uses commas to separate values. A CSV file stores tabular data (numbers and text) in plain text, where each line of the file typically represents one data record.
- RTF (Rich Text Format): A rich text format that can organize and store text information with formatting, convenient for data presentation and editing.
- HTML (HyperText Markup Language): A hypertext markup language that can organize and store text information with tags, convenient for data display and interaction.
- Word: A common document processing software that can organize and store text information in document form, convenient for data editing and typesetting.
- Excel: A common spreadsheet file type that can organize and store text information in table form, convenient for data calculation and analysis.
- PPT (PowerPoint): A common presentation software that can organize and store text information in the form of slides, convenient for data presentation and exchange.
Conclusion
Please feel free to contact the ComPDFKit team for a trial or inquiries. Moreover, we also provide an free online tool for you to experience the convenience and efficiency brought by the ComPDFKit text extraction functionality.