Document Layout Analysis, a complete guide

What is unstructured document layout analysis? How unstructured document impact the training process of your ML model? Here are your answers.

Do you ever struggle to extract information from a document because its formatting is hard to digest? Document layout analysis (DLA) is a field in natural language processing and computer vision that aims to solve this issue. This article dives into what DLA is, its importance, challenges, and some techniques and tools used in this field.

DLA is crucial for machine learning engineers as it's a vital step in developing models that can analyze documents. Without accurate DLA, models may not perform well or may produce inaccurate results.

What Is Document Layout Analysis?

So, what exactly is DLA? It's the process of analyzing a document's spatial arrangement of content to understand its structure and layout. This includes identifying the location of text, images, tables, and other elements, as well as the overall document structure, such as headings and subheadings.

DLA facilitates the processing and analysis of documents in natural language processing and computer vision tasks. By identifying a document's layout, DLA can extract and categorize information and automate document processing workflows. This is especially useful in industries like legal, finance, and healthcare, where processing large volumes of documents quickly and accurately is necessary. Accurate DLA also enhances the performance of downstream tasks like optical character recognition (OCR) and text extraction by providing a better understanding of the document's content and structure.

pipeline-of-DLA

Document Layout Analysis usage among the industry

Industries Use DLA

As we understood the concept of DLA, let’s take a look at the fields that can use it. DLA is used in various industries where large volumes of documents need to be processed quickly and accurately. Here are some examples:

1. Legal Industry: Law firms deal with large amounts of documents such as contracts, legal agreements, and court transcripts. DLA is used to identify key information such as names, dates, and clauses within these documents and to automate tasks such as document classification and retrieval.
2. Healthcare Industry: In the healthcare industry, DLA analyzes medical records, prescriptions, and insurance claims. By identifying the layout of these documents, DLA can help automate tasks such as claims processing, billing, and patient record management.
3. Finance Industry: Financial institutions process large volumes of documents such as loan applications, financial statements, and invoices. DLA is used to identify key information such as account numbers, transaction amounts, and dates within these documents and to automate tasks such as account reconciliation and fraud detection.
4. Publishing Industry: In the publishing industry, DLA is used to analyze the layout of books, newspapers, and magazines to automate tasks such as article extraction and content categorization.

Overall, DLA is an important tool for many industries, as it can help to save time, reduce errors, and improve the accuracy and efficiency of document processing workflows.

Common Applications of DLA

Let's dive into the types of DLA layouts. The layout of a document can be intricate and diverse, and different layouts may require unique approaches for analysis. Here are some of the different types of document layouts that can be analyzed:

Column-based Layouts: These layouts are commonly found in publications, such as newspapers and magazines, that use columns of text to organize information. Due to the varying number of columns and the presence of elements that may span multiple columns, column-based layouts can pose a challenge for DLA.

Table-based Layouts: These layouts are often found in financial reports, scientific papers, and other documents that use tables to present data. Table-based layouts may include complex tables with merged cells and varying column widths, which can pose challenges for DLA.

Form-based Layouts: These layouts are commonly used in forms like applications, questionnaires, and surveys. Form-based layouts usually have fields for users to fill in information and may include checkboxes, radio buttons, and other interactive elements. DLA for form-based layouts may involve identifying and classifying these fields.

Mixed Layouts: These layouts can include a combination of text, images, tables, and other graphical elements arranged in various ways. Mixed layouts can pose challenges for DLA due to the complexity and diversity of the elements within the document.

In addition to these layout types, DLA can identify common elements within documents, such as headers, footers, page numbers, and citations. The specific types of layouts analyzed using DLA depend on the needs and requirements of the particular application or industry.

Different types of layouts require different approaches to analysis, and a range of techniques can be used to address the challenges posed by each layout type.

More Specific…

DLA has been used to solve a real-world problem in the field of medical records processing. In many medical facilities, patient records are still maintained in paper form, which can be time-consuming and error-prone for healthcare providers. To digitize these records and extract relevant information, DLA techniques are used.

For instance, some medical companies use DLA to process medical records and extract key information such as patient demographics, diagnoses, and treatments. Their software can automatically identify the location of relevant fields and accurately extract data, reducing the time and effort required for manual data entry.

By leveraging DLA, healthcare providers can improve the efficiency and accuracy of medical record processing, ultimately enhancing patient care and outcomes.

Types of DLA Layouts

Different types of layouts require different approaches to analysis, and a range of techniques can be used to address the challenges posed by each layout type.

Techniques for Document Layout Analysis

Traditional Methods

Let's explore the methods used in DLA. Starting with traditional methods, include rule-based and template-based approaches.

The rule-based approach involves creating a set of rules that define the layout of the document. These rules are based on the position and shape of different elements in the document, such as text, images, and tables. The rules are then applied to the document to analyze and extract the layout automatically.

In contrast, the template-based approach involves creating a template that represents the layout of the document. The template can be created manually or generated automatically based on a set of example documents. Once the template is created, it is used to identify the position and shape of different elements in the document.

Both approaches have their strengths and limitations. Rule-based approaches are more flexible but can be time-consuming to create and may not work well for complex document layouts. Template-based approaches are more efficient and can be applied across multiple documents but may not work well for documents with varying layouts or inconsistent formatting.

Despite being widely used in the past, recent advancements in machine learning and artificial intelligence have led to the development of more sophisticated methods for DLA.

Limitations of Traditional Methods

Traditional methods for document layout analysis have limitations. For example, they may struggle with complex document layouts, require manual effort to create rules or templates, and may not easily scale to handle numerous documents. As a result, machine learning (ML) has gained popularity for DLA, as it can overcome these issues by learning the layout features of documents from a vast amount of data without the need for explicit rules or templates.

ML algorithms can handle complex document layouts, reduce manual effort, and offer improved scalability and flexibility, making them a more accurate and efficient approach to document layout analysis than traditional methods. By leveraging ML for DLA, businesses can process and analyze vast amounts of documents more efficiently, reducing errors and improving the accuracy of information extraction.

ML-Based Methods

Document layout analysis can benefit from various machine learning techniques, including supervised, unsupervised, and deep learning. By understanding the differences between these approaches, we can choose the most effective technique for a given DLA problem.

Supervised learning involves training a model on labeled data to predict the layout of new documents. In contrast, unsupervised learning uses unlabeled data to identify patterns and relationships for predicting layout.

Deep learning, a subset of machine learning, employs artificial neural networks to learn from large datasets and can tackle complex relationships between input and output data in both supervised and unsupervised learning tasks.

Choosing the right technique depends on the specific needs of the DLA task, and each method has its advantages and limitations.

Challenges and Best Practices

Challenges of DLA

DLA can be a challenging field for machine learning engineers to navigate. One of the most common challenges is the variation in page layouts. The same type of document can have vastly different layouts depending on the designer, publisher, or other factors. This requires engineers to develop robust and flexible algorithms that can handle these varying layouts.

Different fonts and font sizes also pose a challenge in DLA. Character recognition can be difficult when different fonts are used, and text segmentation accuracy can be affected by different font sizes. Pre-processing techniques and feature representations must be carefully selected to classify different types of fonts and sizes accurately.

Noise reduction is another challenge in DLA. Handwritten notes, smudges, or other artifacts can appear on documents and disrupt the analysis. Appropriate noise reduction techniques can help eliminate these unwanted artifacts and improve the accuracy of the analysis.

Engineers can use various techniques such as pre-processing, feature extraction, and algorithm selection to address these challenges. Feature extraction involves identifying relevant features such as lines, edges, and corners from the image. The appropriate algorithm choice depends on the type of document and the specific problem to be solved.

Despite these challenges, DLA has numerous applications in real-world scenarios such as invoice processing, legal document analysis, and digital preservation. The future of DLA looks promising with advancements in deep learning techniques and other machine learning methods.

Best Practices

To achieve accurate document layout analysis, there are various tips and techniques that can be employed. Here are some suggestions:

Pre-processing techniques: Before analyzing a document, it's important to preprocess it to remove noise, correct orientation, and normalize font sizes. Techniques such as thresholding and binarization can be used to enhance the quality of the document image.

Feature selection: Choosing the right features is crucial for accurate layout analysis. Features can be geometric attributes such as line and curve intersections or statistical measures of font sizes and orientations. Techniques like histograms of oriented gradients (HOG), scale-invariant feature transforms (SIFT), and convolutional neural networks (CNN) can be used to extract useful features from the document.

Algorithm selection: Selecting the right algorithm is crucial to achieving accurate layout analysis. Unsupervised algorithms like clustering and segmentation can be used to identify regions of the document with similar attributes, while supervised algorithms like support vector machines (SVMs) and deep neural networks can be used for classification tasks.

Fine-tuning and optimization: Fine-tuning the algorithm parameters and optimizing the feature representations can improve the accuracy of the analysis. This can involve training the algorithm on a subset of the data and then testing it on a larger set to evaluate its performance.

Training data: Adequate training data is essential for machine learning-based techniques. A diverse range of documents with varying layouts, fonts, and sizes should be used to train the algorithms.

By applying these tips, it is possible to overcome the challenges of document layout analysis and achieve accurate extraction of information from the document.

Document Layout Analysis: Final Thoughts

Document layout analysis (DLA) presents several challenges for machine learning engineers to overcome. The most common challenge is dealing with varying page layouts, requiring robust and flexible algorithms to handle the different layouts of the same type of document. Other challenges include dealing with different fonts and font sizes and handling noise in the document.

To overcome these challenges, machine learning engineers can use techniques such as pre-processing, feature extraction, and algorithm selection. It is essential to employ best practices such as pre-processing techniques, feature selection, algorithm selection, fine-tuning, optimization, and training data to achieve accurate extraction of information from the document.

Despite the challenges, DLA has several applications in real-world scenarios such as invoice processing, legal document analysis, and digital preservation. As technology continues to evolve, the future of DLA looks promising, with advancements in deep learning techniques and other machine learning methods.

In conclusion, DLA is an essential field that has a significant impact on our daily lives. Machine learning techniques offer more accurate and efficient ways of processing, organizing, and analyzing information, and have already been successfully implemented in several industries. As DLA continues to evolve, machine learning engineers will play a crucial role in developing new techniques and creating innovative solutions to overcome emerging challenges.

Document Layout Analysis: References

Readings

Github Repositiories

dhSegment (https://github.com/dhlab-epfl/dhSegment): Explore this deep-learning-based tool for historical document processing, with capabilities for segmentation and layout analysis. It's time to bring your A-game to the world of historical documents!
PaddleOCR (https://github.com/PaddlePaddle/PaddleOCR): Check out this incredible OCR system that includes a set of powerful tools for text detection, layout analysis, and text recognition. Developed by Baidu, PaddleOCR is packed with pre-trained models and an easy-to-use API!
Kraken (https://github.com/mittagessen/kraken): Dive into this mighty OCR engine that leverages deep learning for layout analysis, segmentation, and text recognition. Kraken is designed to handle a wide range of document types and languages!
ocropy (https://github.com/tmbdev/ocropy): Discover this OCRopus-based Python library with OCR capabilities, text layout analysis, and document understanding. This flexible tool works with both scanned and born-digital documents!