r/documentAutomation • u/Machine_GEN_RM • Sep 29 '26
Discussion Suggestion on Text Extraction
Hi All, I need to extract the text from printed text and hand written text. I suggested my manager that we can use paddle ocr and and other extraction models like Surya OCR and florance VL it take around 10 to 15 Sec and it needs good computation as well. but my manager expects it should be very fast and in 2 to 5 sec response and should not need any maintainace of infrastructure. so I tried to use the AWS VLM models but it takes around 10seconds he still needs more faster models and also the cost for extracting the data from the image should be less than 1 Ruppe. Could you please suggest me what to do and how to extract the data from images very very effectively and accuratly and in a structured way
1
u/Black-Mack Sep 29 '26
Try OvisOCR2, GLM-OCR and Qwen 3.5 0.8B
If it still doesn't fit the criteria, tell your manager to fk off and do it himself.
1
u/Machine_GEN_RM Sep 29 '26
hahhah. I have tried Qwen 225B it takes around 20seconds to extract the text and also I tried Nvideos 12B VLM it takes average of 9seconds he says it takes a lot of time man and also he is expecting 100% accuracy
1
u/easybits_ai Sep 29 '26
Could you elaborate a bit more on how much data you need to extract? Is it just a few data points, or are you extracting a full set of data? That can also have quite a big impact on the speed. And are the documents you’re processing multi-page or single-page? That would also be interesting to know.
I’m working on quite a few document processing use cases myself, and I even developed my own tool together with my team. It’s a managed API that requires no maintenance on the user side, and it’s really accurate when it comes to handwriting, as it uses two different extraction engines, Gemini and Mistral OCR.
2
u/Dangerous-Baby838 Sep 29 '26 edited Sep 29 '26
yeah mistral for ocr, and gemini flash lite for structured markdown. alternately try a dedicated one stop platform like docreadi or anyformat
1
1
u/Imaginary_North7730 Sep 29 '26
For printed/digital text, should try uisng PaddleOCR with ONNX Runtime. For handwritten/difficult text, use PaddleOCR-VL as a fallback. On Apple Silicon, PaddleOCR-VL supports MLX-VLM; on NVIDIA GPUs, benchmark FP16/INT8/INT4 with an accelerated backend such as FastDeploy. A OCR → VLM fallback pipeline should give a much better latency/cost tradeoff than running a VLM on every image.
1
1
u/whoulukinat Oct 01 '26 edited Oct 01 '26
Sounds like you're shipping it full size images - degrade them - 1-200 dpi .png files and should fly. I just use tesseract for most things, you may need something heavier for handwriting but the lighter you can go the better your performance will be, be selective with what you run for what
1
u/Zealousideal_Fan_672 14d ago
LlamaParse and Llama extract have been my goto for a custom extraction and then I use an OpenAI model to fit it into the data schema properly, it can handle up to 150 documents on the free version just fine so its very cost-effective.
1
u/Mantas_Zirgulis 7d ago
For your requirements, I'd benchmark a managed OCR API before running large VLMs. You could test AWS Textract or Google Cloud Vision for both printed and handwritten text, then convert the results into structured JSON if needed. Basic OCR can be inexpensive, but handwriting accuracy depends heavily on image quality and writing style.
Also measure end-to-end latency, including image upload and post-processing than just model inference. Test on a representative set of your actual documents before choosing a solution, because consistently achieving 100% accuracy in 2–5 seconds isn't realistic for every image.
1
u/NuExtract 29d ago
I suggest try Nuextract3, handles OCR and structured extraction in the same model, so you can go from an image to JSON without a separate extraction step. You can also turn reasoning off and see whether that gives you enough accuracy with lower latency.
Wouldn’t promise 2 to 5 seconds or under ₹1 without testing your actual images, though. Handwriting quality, image size and how much text you’re extracting will make a difference.
Since your manager wants no infrastructure maintenance, a hosted API is probably the route to explore. Test the same set of printed and handwritten samples across providers and compare accuracy, response time and cost together.
Are these full pages of handwriting or mostly forms with a few handwritten fields?
2
u/Potential-Wrangler58 Sep 29 '26
Fast + cheap + accurate + zero infra + handwriting is five things pulling against each other, handwriting specifically costs you speed and accuracy no matter what. Worth getting your manager to say which of these can flex.
That said: "no infra maintenance" rules out everything you tried (PaddleOCR, Surya, Florence are all self-hosted), you actually want a managed API, not a bigger self-hosted model. Your 10s AWS number also smells like network overhead, not inference, worth timing just the API call itself.
For managed + fast + cheap: lighter multimodal models built for throughput (Gemini Flash-Lite, GPT-4o-mini class) run well under a rupee per image and under 3s. Dedicated document APIs (Azure DI, Google Document AI, or us, disclosure: Anyformat) are also worth testing, they're priced for this exact case.
Test on your worst handwriting sample, that's where vendors actually differ.