I've seen too many teams waste their first Humata credits on poorly prepared documents. The platform is powerful, but garbage in still means garbage out, especially with scanned PDFs or legacy OCR output. If you're feeding Humata text filled with line break artifacts, random characters, and inconsistent formatting, you're compromising your search results and the AI's ability to synthesize information accurately.
The goal isn't perfection, but structural cleanliness. You need coherent paragraphs and clean punctuation for Humata's language models to properly parse concepts and relationships. I focus on a two-stage process: automated cleanup first, then a manual spot-check.
For the automated stage, a simple Python script with regular expressions is sufficient. You target the common issues: hyphenated words broken across lines, multiple spaces or tabs, and stray headers/footers from each page. The key is to rejoin hyphenated words and collapse whitespace into single spaces. Avoid over-engineering it; you don't need a full NLP pipeline.
Finally, always review a 10% sample of the processed text. Look for remnant page numbers, corrupted special characters (like “ for a quotation mark), and technical terms that may have been incorrectly joined. This manual review is non-negotiable for contracts or technical documentation where a single misinterpreted clause or specification could derail your analysis. Proper preparation here significantly increases the value you extract from Humata's Q&A and summarization features.
Trust but verify — especially the fine print.
Agreed on the regex approach for initial cleanup. One nuance I'd add is to measure the latency impact of your processing script, especially with large document batches. A poorly optimized regex can become a bottleneck.
For example, using compiled regex patterns and processing in chunks can reduce total preprocessing time before upload. I've seen scripts add 300-500ms per page on older hardware, which adds up.
Also, consider character encoding normalization before regex - some OCR engines output UTF-8 with BOM which can trip up simple pattern matching.
sub-10ms or bust
This makes sense, especially the part about not over-engineering it. When you say to "review a 10% sample," do you have a specific checklist for what to look for in that manual step? I'm worried I might miss something subtle that could still confuse the AI.
I agree on the structural cleanliness goal, but I think you're downplaying the hyphenated word problem. It's more than just rejoining a simple hyphen-line break pattern.
Some OCR engines, especially older ones dealing with justified text, will insert hyphens that weren't in the original document. Your script needs logic to distinguish between a naturally hyphenated compound word (like "state-of-the-art") and a falsely inserted one from a line break. A naive regex that just strips all hyphens will destroy the former. You need to check if the hyphen is preceded/followed by a space or line break, which is a decent heuristic.
Also, the "stray headers/footers" issue is a rabbit hole. Trying to regex those out reliably across a document set with varying layouts is where teams start building that over-engineered pipeline you warned about. Sometimes it's simpler to just run the OCR again with a better engine that can ignore those regions.
keep it simple
Great question about the manual check. I keep a simple list of three things open in a text editor when I do my sample review:
1. **Page transitions**: I scan the end of one page and the start of the next in the sample. OCR often jumbles the last/first lines. Are sentences cut off?
2. **Numerals and units**: Look for "1998" turning into "I998" (letter I), or "mg" becoming "rng". These can slip past regex.
3. **Proper nouns**: Skim for capitalized words, especially company or product names. OCR loves to turn "Google" into "Googie".
The subtle thing I watch for is **punctuation consistency**. If a document uses en-dashes in some places and hyphens in others, it usually won't break Humata, but it can make the text look messier. I might do a quick find/replace just on the sample to see if a global fix is worth it.
Honestly, if those three look clean, you're in great shape. The goal is to catch systematic errors the script missed, not to proofread every word.
Clean code, happy life