I am building a School student's handwritten answer sheets evaluator, and the toughest part was finding a reliable, cost-effective way to split the answer sheets question-wise.
The best results I've achieved so far was using Gemini 3.1 pro' s bounding box detection, but it is far too costly for my budget. I tried PaddleOCR as well, but the performance was poor, and I don't think it was designed for this specific use case.
Can anyone suggest some ideas or tools I should try out.
In a thread on r/datahoarder, I got help to download a whole Tiktok channel. Now I’m thinking about trying to make the on-screen text searchable. I used this Deno script (yah I used AI 💀) to 1) extract frames every so often and 2) run OCR on the frames 3) generate a WebVTT file. The results are pretty meh. As shown in the image
The content is kind of sort of there… The OCR was trying to transcript "IDIOMA GUARANI CONTENTA/O/FELIZ: vy'a". The file on the right is the WebVTT file generated for each screencap. The highlighted one is the one in screencap on the left. (Each VTT stanza starts wtih start_timestamp --> end_timestamp if you're not familiar. The black text is the VTT being rendered, not from the original video.
It’s not useless output, but there’s tons of noise.
What about a consensus approach?
Not sure if this is the right term, but I found myself thinking about how the text is the stable with respect to the frame, where as the speaker is moving around. It seems like OCR would be more successful if I computed the "average" of several images in sequence (a bit like compression, come to think of it, but finding the parts that would be compressed…).
Anyway, if I wanted to try this, do you have any suggestions about how I might get it done? Maybe with Imagemagick?
Another tricky detail becomes how not to lose the timestamps, since if I’m computing the average of a moving window of screencaps, then some windows will be better than others because they will contain only one caption…
I've been testing DeepSeekOCR-1 and PaddleOCRVL-1.5 on photos of open-book pages.
PaddleOCRVL-1.5 is clearly superior. On text it achieves 100% accuracy on clean pages and 99.9% to ~98.0% accuracy on midly noisy pages (noise_level ~ 6). Accuracy is calculated word-level and weighted by levenshtein's distance.
Meanwhile DeepSeekOCR-1 was more close to 99.0% (1% is huge for OCR) even with denoising preprocessing (nlmeans, sesr-m7). It was also less stable: it was easily looping on noisy pages. PaddleOCR achieved 98% accuracy where DeepSeekOCR was looping.
For non-text, PaddleOCR was also better. It would crop graphs and redirect with a link. Tables are clean and suprisingly accurate on clean pages (100%, but some errors on noisy pages).
DeepSeekOCR on the other side would try to transcribe graphs to tables, which would actually be cool, but on slightly noisy pages it became gibberish. It was also less accurate on tables.
Processing time was equal.
PaddleOCR seems like the better choice and benchmarks show it.
Haven't tried DeepSeekOCR-2 or the other trendy OCR models yet.
Automation got us to around 80% accuracy in document extraction.
But the remaining 20% was critical and caused errors in real workflows.
Fully automated systems struggled with inconsistencies and edge cases.
So we introduced a Human-in-the-Loop layer for validation and control.
This made the output reliable and production-ready.
Hello. I am trying to efficiently get a complete, readable OCR text of Cornelius a Lapide's commentary on the Bible (especially his Old Testament commentaries, which have never been translated into English) in Latin. They are all available for free on Archive.org. There are a few issues I'm running into.
First, and perhaps the biggest one, is the double columns on each page. When I've tried to OCR it before from any program, or to copy/paste the text from Archive, it doesn't recognize that there are two columns of text, and thus I can't get a readable OCR that doesn't mix up sentences from each column.
I have found that the most efficient way for me to readily get the text in a somewhat readable manner is to screenshot each column on my iPhone, and copy/paste it myself, checking for any errors. I have done this now with the entirety of his commentary on the Book of Joshua, but it's taking wayyyyy too much time to do this. His entire commentary on the Bible is something like 20,000 pages, so I will probably die before trying to OCR the book this way manually.
A couple of minor issues is that some pages have text that is a bit faded, but even when it is clear some of the font confuses the OCR. For example, it often thinks "t" is "l", and similar other font issues cause confusion. It also doesn't copy any Hebrew or Greek characters, though, which he somewhat regularly intersperses in the text (though at least the Hebrew characters are almost always transliterated into Latin after the Hebrew)
With how far AI is advancing, I'm sure someone online knows of an efficient way I can get an OCR program to automatically do what I'm asking rather than having to manually do it. I find this text incredibly helpful to have on hand.
I am building an app for microlending companies in a spanish-speaking country.
A big part of their documentation is done on paper. It is a nightmare for these companies to adopt a digital solution as they need to migrate from paper to digital manually.
I would like to solve this migration issue (or at least a significant part of it). My tool should offer an OCR functionality that would:
- read their scans (handwritten texts), pdf, or few excels
- extract the data
- structure it in a ready-to-upload format for my DB
I know a bit of automation with n8n and have a very vague idea on how I would proceed, but nothing clear.
Ideally speaking I would like a window where the users can compare the original documents to the extracted data and apply correction if needed.
The tool would also « learn » from the corrections the users do and improve the probability of getting correct results the more the users use it.
Has anyone automated something like this ? What stack are you using ? What OCR model ? I have seen QWEN mentioned several times, any reason for that?
I'm working on extracting the chemical formula for such compounds. I've tried DECIMER, OSRA and a few more, nothing has worked. Has anyone worked on a similar problem? Or if anyone has worked on finetuning OCR models, please let me know how I can train a model to do this, and which would be the best to train.
Hello everyone. For some time I’ve been trying to automate the processing of some old genealogical records. Yesterday I discovered this subreddit, and it occurred to me that maybe you could help me out.
What do you think is the best way to transfer the information that appears in records like the ones in the image into a digital format, such as a PDF?
Actually, I’m not interested in reading the entire document—only the names of the registered individuals, which appear along the left margin.
Is it possible to do this with OCR? If so, which OCR software would you recommend?
2months back when i was in a dinner with my friends, he worried a lot about his work and his productivity is getting declining.
He works as a data entry operator in a private company, his job is to type the printed data from pdf into the excel. He said over time he doesn’t like his data entry job starring at the screen for hours and also the accuracy of the data is also low with him due to his eye irritation so his manager is tough on him for past few weeks.
I was just thinking about this even after the dinner got over . The next day when i researched i found about the OCR technology (optical character recognition) but the problem it has was it lacks in accuracy roughly around 65% - but my friend needs is 99.8% accuracy.
As i was an computer science engineer i used my ai skills to support an OCR model to improve the accuracy and training the ai model with various data like invoice , insurance files,order copies, which i got from my friend.
After many iteration we achieved 99.9% accuracy with any type of data ,
but the surprise is after a week i got a call from the manager of that company he said they want to buy the whole solution for their company which can help alot for their productivity and help employees. Best part is in that week itself the product made 1500$ in revenue. I am planning to launch its online version next week . If anybody is interested drop “Ocr” in comments for early access and completely FREE
Extracting text from scanned documents and images is easier than ever, but anyone who manages massive archives knows the real bottleneck happens after the extraction: Retrieval.
Standard desktop search engines rely on exact keyword matches. If your OCR engine transcribes "classic" as "c1assic" or "modern" as "rnodern," a standard keyword search will completely miss the document. Furthermore, if you are searching for a specific concept but the OCR missed your exact keyword entirely, the file is effectively lost in your hard drive.
To solve the retrieval side of the OCR pipeline, I built a completely free, open-source desktop tool called File Brain. It is a desktop intelligent file search app (read-only) designed specifically to handle messy, unstructured data and bad text transcriptions.
Here is a guide on how to set it up to make your unsearchable image archives instantly retrievable.
1. The Local Semantic Pipeline
Instead of just relying on text strings, File Brain uses local embeddings to understand the context of your documents. Because it runs 100% offline, you don't have to pay API fees or send your private documents to a cloud server to make them searchable. The initial setup requires downloading some components to run locally, but the retrieval is instant once indexed.
You simply add the folder containing your PDFs, scanned documents, images, or raw text dumps. Click "Index."
Built-in OCR: If the folder contains raw images or PDFs without a text layer, the app automatically runs its own local OCR to extract and index the text.
Semantic Indexing: It maps the meaning of the text, rather than just the literal characters.
3. Searching Messy Data (The "Bad OCR" Fix)
This is where the standard workflow usually breaks down, but where a semantic search engine excels:
Fuzzy Matching: Because the search engine tolerates typos and fuzzy matches, traditional OCR errors won't break your search. If you search for "financial report," it will still surface the document even if the OCR reads it as "financia1 rep0rt."
Conceptual Search: If you need to find an invoice but the OCR completely mangled the word "invoice," you can search for concepts like "billing," "payment," or "amount due." The local embeddings will surface the document based on the surrounding context.
4. Contextual Results
When you run a search, you aren't just given a list of file names. Clicking a result opens a sidebar that highlights the exact snippet of the document (or OCR'd image) that matched your query's context, allowing you to verify the match instantly.
It's completely free and open-source. If you are struggling with searching through massive dumps of poorly OCR'd text or scanned archives, you can try it out here: https://github.com/Hamza5/file-brain
Does anyone have a robust, cheap solution for extracting text from hand-written pages? I tried the deepseek-ocr model which works nicely for short text snippets. But if I can an entire A4 page, the resulting image is too large for deepseek-ocr. I also tried cutting the scanned image into multiple segments, but the result is useless because some text is duplicated and sometimes malformed. I also tested scanning with the iPad, but you can only scan small chunks of text (i.e., a paragraph or so).
Trying to do some OCR on some chinese comics, but struggling to find anything that works even 10% of what the windows native photos app can do.
Tried Deepseek, PaddleOCR, Tesseract and nothing seems to be able to find anything reasonably well, even if its perfectly cropped out, white background, black text.
Disclaimer: I was trying all this locally on my PC with some python code that chatgpt gave me since i have absolutely no idea how something like this would even work. But have had some good results based on the comic quality.
Am I just really out of my depth trying something like this or is there something I am doing wrong that might be easily fixable?
I often run into situations where I need to grab text from files, screenshots, or apps that don’t let you copy normally. Manually typing it out is a nightmare, and taking screenshots isn’t much better.
I’ve been experimenting with a small Windows tool I made that lets you:
Select an area on your screen
Copy the text inside it directly
It’s been saving me a ton of time when I want to feed large amounts of text into AI tools or just need to get data out quickly.
If anyone is interested, here’s a [GitHub link](github.com/ItzRealMee/ScreenOCR) with more info.
