Parsing: Definition, Use Cases, and Key Tools

What is parsing?

Parsing, also known as syntactic analysis, is the process of automatically analyzing a data structure or raw text to extract elements that a machine can interpret. It is a key step in many areas of computing, including code compilation, document analysis, information extraction, and web scraping.Parsing is used when content such as a file, a web page, or a text stream needs to be understood, structured, and transformed to feed into software, a database, or an analytical algorithm.

What is parsing in computer science?

In computer science, parsing is used in a wide range of contexts  from translating source code into machine instructions, to analyzing configuration files, or processing structured languages like HTML, XML, or JSON.The core idea remains the same: decoding an input (often textual) based on predefined rules (such as grammars or formats) to make it usable by a program.In the context of document processing, parsing is applied to PDF files, emails, or scanned documents to automatically extract information such as names, amounts, dates, or reference numbers.

What is file parsing?

File parsing refers to the automatic analysis of a file’s content to extract useful data. This can apply to various file types:

• Structured files (JSON, XML, CSV): tags, nodes, or fields are identified to feed into a database or software.
• Semi-structured files (PDFs, forms): text zones are detected based on position, style, or keywords.
• Unstructured files (images, scans, handwritten documents): OCR is often required to read the content before parsing.

In a real-world example, parsing a PDF invoice means automatically extracting elements such as the total amount, date, supplier name, or line items and integrating them into an accounting system.

What is a parser?

A parser (or syntactic analyzer) is a program or software module designed to perform this analysis.
It follows a formal grammar or parsing rules to recognize expected structures within the content.

There are different types of parsers:

• Lexical parser: breaks the text into meaningful units (words or tokens).
• Syntactic parser: builds a hierarchical structure (syntax tree) from the tokens.
• Domain-specific parser: adapts extraction rules to a particular context (e.g., invoices, contracts, forms).

In document parsing, the parser is often combined with an OCR engine, an NLP model, or rule-based extraction to identify key information within a file.

How does parsing work?

The parsing process involves several steps, which vary depending on the type of document and the chosen approach (rule-based, AI-driven, or syntactic analysis).

Typical steps in document parsing include:

• Preprocessing: cleaning the document and applying OCR if needed.
• Tokenization: breaking the content into words, lines, or blocks.
• Key element identification: detecting target fields such as amounts, dates, names, etc.
• Structuring: organizing the extracted data into a usable format (table, database, JSON, etc.).
• Validation: checking the quality of extracted data and handling errors.

These steps are often combined with document automation tools that enhance both the reliability and performance of data extraction.

Main Use Cases of Parsing

The need for automated data extraction exists across nearly every industry that handles documents. Here are some typical use cases that illustrate how parsing delivers value in different fields:

Finance & Accounting

Human Resources

Legal & Public Sector

Logistics & Supply Chain

Other Notable Sectors

Tools and Languages for Parsing

Document parsing relies on a set of software tools and programming languages designed to automatically extract, structure, or interpret content from digital files. Choosing the right technology is a key success factor in any document automation project.

There are two main approaches to implementing document parsing: using technical tools (parsing libraries integrated into code) or relying on ready-to-use application solutions like Koncile. The table below compares these two types of tools based on their use cases, user profiles, and levels of abstraction.

Languages Commonly Used for Parsing

• Python: the most widely used language for document parsing, thanks to its rich ecosystem (OCR, NLP, PDF extraction, etc.).
• Java: often used in enterprise architectures for building robust, scalable parsers.
• JavaScript: useful for parsing JSON or interacting with the DOM of web pages.
• Bash/Shell: suitable for parsing simple text files, logs, or CLI outputs.

These technologies enable the development of document parsing pipelines tailored to business needs including field extraction, classification, structuring, and semantic enrichment.

How Does Syntactic Analysis Work?

Syntactic analysis is an advanced parsing method that breaks down a text into grammatical elements to understand its logical structure. It goes beyond simple keyword extraction by identifying relationships between words — such as the connection between a subject, verb, and object.

In document parsing, this approach enables accurate interpretation of natural language content like contracts, reports, emails, or legal documents.

Syntactic parsing allows software to:

• Understand sentence structure (e.g., "The tenant agrees to pay a monthly rent of €750")
• Identify entity relationships — who is doing what, to whom, and under what conditions
• Extract data with greater context, even when phrasing varies (e.g., "monthly rent of €750" vs. "€750 in rent")
• Detect linguistic dependencies to avoid incomplete or ambiguous extractions

In short, syntactic analysis goes beyond surface-level reading: it helps reconstruct the meaning of the text — a crucial step when dealing with complex documents.

This process typically involves several steps:

• Tokenization: breaking text into units (words, punctuation, etc.)
• Part-of-speech tagging: identifying the grammatical role of each word (noun, verb, adjective, etc.)
• Dependency parsing: building a graph that maps each word to its syntactic function (subject of, complement of, etc.)
• Syntax tree generation: creating a hierarchical representation of the sentence, usable by rule-based systems or AI models

This parsing workflow is handled by modern NLP engines, often trained on large multilingual datasets.

The Main Approaches to Document Parsing

Document parsing can rely on different technologies, each suited to a specific context or type of document.
The three main categories are: syntactic parsing, rule-based extraction, and AI/NLP-based approaches.

Syntactic Parsing

This method relies on analyzing the grammatical or logical structure of the text. It is commonly used to process natural language documents (contracts, reports, etc.) by identifying relationships between words (subjects, verbs, objects, etc.).In semi-structured documents (such as logs or XML files), it uses formal grammars to extract blocks of information.Syntactic parsing offers high precision when the structure is known in advance, but it lacks flexibility when documents vary.

Rule-Based Extraction

Here, extraction is based on manually defined rules such as regular expressions, fixed positions, or keywords. This method is effective for homogeneous documents like standardized forms, invoices, or bank statements.It offers full control over what is extracted, but it is rigid  any change in document format requires the rules to be updated. For simple and repetitive use cases, it is often the fastest solution to implement.

Artificial Intelligence and NLP

AI-based approaches (machine learning, deep learning) learn to extract data from annotated examples.
By combining layout analysis with semantic understanding, they adapt to a wide variety of documents  even unstructured ones.
Models like LayoutLM achieve high accuracy rates and continue to improve over time through human corrections.
This method is ideal for processing large volumes and diverse formats but requires an initial investment in annotation and model training.

Why Use Parsing in Business?

Document parsing offers numerous practical benefits for businesses looking to automate and secure their document processing.

In short, using document parsing in a business context means automating repetitive tasks, improving data accuracy, and boosting operational efficiency — often with a fast return on investment. In many cases, an OCR/AI project pays for itself within just a few months thanks to the hours of manual work saved.

Tips for Choosing the Right Parsing Solution

Given the wide range of tools available on the market, it’s essential to identify the solution that best aligns with your use cases, document types, and technical constraints. Here are the key criteria to consider for making an informed decision.

Volume to Process and Expected Speed

Start by assessing the number of documents to be processed (daily, monthly) and the level of responsiveness required.

• For high volumes or near real-time needs, opt for scalable solutions that can handle increased workloads such as cloud-based services or multithreaded OCR engines.
• Conversely, for smaller volumes, a lightweight local tool or open-source solution may be sufficient.Also check whether the tool supports batch or parallel processing, especially if you experience activity spikes.

Document Diversity and Complexity

The type of documents you handle greatly influences the choice of technology.

• Structured and consistent documents (such as CERFA forms or bank statements) can be processed effectively using rule-based methods or simple models.
• More diverse content with free or loosely standardized formats typically requires AI and NLP-based approaches.
• If your documents include partially or fully handwritten content, make sure the tool supports handwriting recognition (ICR).For text-heavy documents (emails, contracts, etc.), the quality of language processing  such as multilingual NLP or French language support  is a key factor to consider.

Available Technical Expertise

Your organization’s technical maturity will largely determine the type of solution that’s feasible.

• If you have a data or tech team, you may consider a custom development based on open-source components
• However, for a faster deployment or if internal resources are limited, a ready-to-use solution with dedicated support is the better choice.
• Also think about ongoing maintenance: who will update the models, adjust the rules, or oversee extraction quality?The level of support offered by the provider is a key decision factor.

Hosting and Security Requirements

The deployment model (cloud or on-premises) depends on your internal policies and regulatory requirements.

• Cloud solutions offer agility, simplified maintenance, and automatic scalability.
• However, if you handle sensitive data (e.g. in healthcare, finance, or legal sectors), an on-premise solution or one hosted in a certified private cloud is often a better fit.
• Be sure to check security guarantees such as GDPR compliance, HDS hosting, ISO 27001 certification, and more.

Budget and Return on Investment (ROI)

Your budget will naturally influence your choice, but it should be weighed against the expected productivity gains.

• Open-source solutions are inexpensive upfront but require significant development and ongoing maintenance.
• Commercial solutions come at a cost but are often faster to implement and deliver ROI more quickly
• Some platforms operate on a consumption-based model (per page or per document), which can be cost-effective at the start but be mindful of rising costs as volumes grow.A simple ROI analysis (time saved, errors avoided, reduced manual entry) can help make an objective decision.

Try Before You Buy: Proof Through Practice

Before making any commitment, run a Proof of Concept (PoC) using a representative batch of documents.

• Test multiple solutions and assess their accuracy, usability, field extraction capabilities, and API integration with your existing tools.
• Pay close attention to how errors or edge cases are handled (e.g., poorly scanned documents, unknown formats).
• Also evaluate the quality of support: responsiveness, guidance, documentation, and overall assistance.

Parsing – Key Takeaways

Parsing plays a key role in automating document processing. Whether syntactic, rule-based, or powered by artificial intelligence, it transforms unstructured documents into usable, structured data.

It delivers major benefits for businesses, including:

• Time and productivity gains
• Reduced human error
• Reliable, traceable data
• Automated document workflows

The right parsing technology depends on your document types, processing volume, available technical resources, and desired level of automation.

To ensure a strong return on investment, it’s recommended to:

• Run a proof of concept (PoC) using real documents
• Evaluate extraction accuracy and integration ease
• Analyze scalability and the quality of technical support

In summary, document parsing is a foundational step toward intelligent information processing. When properly implemented, it paves the way for faster, more reliable, and more efficient document management.