A monolingual corpus is a large, structured collection of texts in a single language. It is the foundational dataset for nearly all modern language technologies, from spell checkers and predictive text to Large Language Models (LLMs). After you've digitized your books and documents with OCR, you're left with raw text that needs to be refined into a clean, usable corpus.
The Challenge of "Dirty" Text
OCR output is rarely perfect. It often includes unwanted text such as page numbers, headers, footers, publisher's notes, and OCR errors. Using this "dirty" text to train a language model will teach it incorrect patterns. Cleaning is a mandatory step.
Methods for Cleaning Raw Text
There are several ways to approach text cleaning, ranging from intelligent AI-driven methods to more technical, hands-on approaches.
Method 1: The High-Quality Path with Vision LLMs
The simplest and often most effective method is to use a Vision LLM (like GPT-4o). You can feed it the scanned page images and instruct it to extract only the main content, intelligently ignoring irrelevant parts. While this can be the most expensive option for large-scale projects, the quality of the output is often superior because the model understands the layout and context.
Example Prompt:
"From the attached image, extract only the main body of text. Ignore all headers, footers, and page numbers. Correct any obvious spelling or OCR errors based on the context of the sentence."
Method 2: The Local & Technical Path
For large-scale processing where cost is a concern, local tools offer more control.
- Bounding Box (BBOX) Extraction: For books with a consistent layout, you can define a rectangular "bounding box" that contains only the main text content. Then, you can run an OCR process that is restricted to this specific area on every page, automatically ignoring headers and footers.
- Advanced Layout Analysis (Docling): For complex layouts with multiple columns or mixed content, specialized models are needed. Docling is an open-source tool that can analyze a page's structure and separate different text blocks. This is a powerful option but requires a dedicated GPU server and technical expertise to run locally.
Validation: The Dictionary as Your Quality Check
No matter which cleaning method you use, you must validate the output. Your digitized dictionary is your most powerful tool for this task. The process is straightforward:
Extract Unique Words
Take your entire cleaned corpus and generate a list of all unique words that appear in it.
Compare Against the Dictionary
Compare the list of unique words from your corpus against the list of headwords from your digitized dictionary.
Identify Potential Errors
Any word that is in your corpus but NOT in your dictionary is a candidate for being an OCR error, a typo, or a new word. This gives you a targeted list of items to manually review and correct.
This validation process also works in reverse! You might discover legitimate words in your corpus that are missing from your dictionary, providing an excellent opportunity to expand and improve it.
The Final and Most Important Step: Share Your Corpus!
A monolingual corpus is most valuable when it's used. The largest technology companies are constantly training new, more powerful LLMs. They do so by scraping vast amounts of public data from the internet. If your language's corpus remains on a private hard drive, it will be invisible to these training processes, and future AI models will not know your language.
Publish on Hugging Face
The best way to ensure your language is included in the future of AI is to make your corpus publicly available. We strongly recommend uploading your cleaned, high-quality corpus to the Hugging Face Hub. It is the central repository for machine learning datasets, and it's where AI researchers and developers look for data. By publishing there, you give your language a voice in the next generation of technology.
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Explore DatasetsWhat Comes After a Monolingual Corpus?
With a clean corpus, you can train language models. But to build translation tools, you need texts aligned with their translations. This is known as a parallel corpus.
Learn About Creating a Parallel Corpus