Most OCR libraries work great until you start asking uncomfortable questions:

Does import trigger a network request? Where are the model weights stored? Why does a simple OCR task need hundreds of megabytes of dependencies?

I wanted something different, so I built wormOCR - a lightweight, fully local OCR engine for printed and handwritten English.

The goal was simple: make OCR small, fast, predictable, and genuinely offline.

What makes it different?

• No surprise downloads: import wormocr never triggers a network request.

• Explicit model downloads: Run wormocr download once to get the weights.

• Verified weights: Downloaded from a pinned GitHub release and checked using SHA-256.

• Fully local inference: No cloud APIs, telemetry, or network dependency.

• MIT licensed: Open source and free to use.

Two models, different trade-offs

How fast is it?

Measured on my laptop:

Single-line inference (wormocr-2M):

• CPU: ~14 ms end-to-end.

Full-page inference (same 3 digital pages):

The model weights range from 8.7 MB to 33.5 MB, compared with approximately 132 MB for the PaddleOCR medium stack in my comparison.

These are my own measurements on a small test set, not a comprehensive benchmark. Hardware, configuration, and preprocessing matter.

Try it yourself

pip install wormocr
wormocr download

from wormocr import OCR

ocr = OCR()
result = ocr.predict("invoice_line.png")

print(result.text)

Where it stands today.

Clean printed text and receipts are strong. Messy notes and historical handwriting are usable, but still challenging.

Currently:

• English printable ASCII only.

• Classical page-layout detection using projection profiles, rather than a large learned detector.

• Two model sizes with different speed/accuracy trade-offs.

I'd rather be transparent about what doesn't work than market this as a universal OCR solution.

GitHub: https://github.com/arjun988/wormOCR

PyPI: https://pypi.org/project/wormocr/

I'm particularly interested in feedback from people working on OCR, computer vision, edge AI, and offline-first software.
If you find this interesting or think it could be useful, I'd really appreciate a ⭐ on GitHub. It helps the project get visibility and motivates me to keep improving it.
Feedback, issues, PRs, and stars are all very welcome!

Would you actually use an OCR engine this small in a production application? What would you want to see improved first?

i’m a ceng student.‬
‪i created a from-scratch ai + robotics learning path repo to build a solid foundation in the age of AI.‬
‪content:
math → cs/programming → ml/dl → vision → slam → robotics → uav systems → autonomy flight.
‪those who want to progress together can star it‬
‪https://github.com/emrealtindag/ai-pathq

I made a one-page interactive guide to the original ViT paper: https://vit-in-depth-671a9f.web.app

You can load an image and step through patching, embedding, attention maps and attention rollout, with tensor shapes and PyTorch code alongside. It runs entirely client-side. It also covers the patch-size/compute trade-off and position-embedding interpolation for higher-resolution fine-tuning.

Feedback and corrections are welcome, especially on anything I've explained badly.

Mercor is hiring: Scientific / ML Engineer (remote, contract).

• Pay: $90 – $140 / hour

Apply: https://t.mercor.com/NU4Fj *Disclosure: this is my referral link; I get a bonus if you're hired.*

Given an image, what is the best way to classify it as being "interesting" as in having some distinguishable macro scale structure (from blobs, patterns, or real objects) vs "uninteresting" (pure static noise). In the uninteresting case, ideally you could also assign this class to images that are just blank as well or maybe a singular pixel has been changed

I'm building a hobby ball and player tracker for amateur padel, using fixed club cameras. I found the PadelTracker100 dataset (dense ball, pose and shot labels), but the two source match videos on YouTube are now private. The labels are no use to me without the frames.

The two videos are the 2022 Barcelona Premier Padel finals, women's (Salazar/Triay vs Josemaría/Sánchez) and men's (Lebrón/Galán vs Di Nenno/Chingotto), the PadelTracker100 source matches

Does anyone have either of these?

1. ⁠A copy of the two videos, or

2. ⁠The extracted frames that match the labels, or

3. ⁠Contact details for the authors, since the emails I tried bounced.

4. ⁠Also happy to hear about other padel datasets with ball labels. Public ones, or ones where the authors share on request.

5. ⁠Thanks!

For a personal drone project i wanted a object detection model that runs on high resolution on tiny hardware(only 2.6M parameters) I started with the usual yolo-11n and noticed it sucked ASS! Soooo i trained it trough a few thousand samples of a drone detection dataset and now its about as converged as a 2.6M model can get. The exact benchmark scores are on the HF page:) Hope someone can do sth with this XD

Get the model here: Polymanius/skyPeak-11n · Hugging Face

Recently I benchmarked Qwen3-VL-2B on more traditional vision tasks, and thought it would be interesting to see how much changes with the much larger 235B model.

Quite interesting results:

• Classification - excellent: 96% top-1 accuracy.

• Attributes - good: 73.86% accuracy.

• Detection - still poor: 16.67% [email protected]. Interestingly, precision is 90.67%, but recall is only 9.18%. The model is quite conservative in what it detects.

• Grounding - modest improvement: 31% box accuracy and 41% point-in-box accuracy. However, 53% and 50% of samples respectively failed to produce valid results. This higher failure rate may be related to the benchmark or remote inference setup rather than the model itself, so I'll investigate further before drawing conclusions.

I'll also prepare a comparison chart between the two models tomorrow to make the differences easier to see.

TL;DR: Scaling up improves most tasks, particularly attributes, but doesn't seem to solve the localization problem. Detection recall and grounding reliability remain major weaknesses.

Benchmark results, methodology and code: https://github.com/Tenkei/vlm-tasks/tree/main/benchmarks#qwen3-vl-235b

Hi everyone!

I've been working on an AI-powered road accident detection and monitoring system, and I'm excited to share my progress.

The goal is to explore how computer vision can help identify potential road accidents and improve incident monitoring, particularly in environments where road safety is a major concern.

What I've implemented so far:

• YOLO11 for object detection.

• Pose estimation for analysing human movement.

• BoT-SORT for object tracking.

• Accident-related event detection and monitoring.

• Near-miss and sudden-stop event monitoring.

• Telegram alerts for critical events.

• SMS notifications using Africa's Talking.

• A dashboard for monitoring video feeds and detected events.

I'm currently around 80% through development. I'm still working on improving detection reliability, reducing false positives and refining the alerting system.

This is an independent project I'm developing from Kenya 🇰🇪.

I'd genuinely appreciate feedback from people working in:

• Computer Vision

• AI/ML Engineering

• Object Tracking

• Intelligent Transportation Systems

My questions:

1. What would you improve in this system?

2. What additional features would make it more useful in real-world road monitoring?

3. Are there any approaches you'd recommend for improving accident detection reliability?

I'm happy to learn from your feedback and suggestions.

Thanks for taking the time to read about my project!

Disclosure: we make this. We recorded a clean install of our ANPR SDK 4.0 end to end, nothing staged: pip install, a 12-line demo that prints the plate it read and the OCR confidence, then a local REST server with a dashboard that shows each reading. It runs on CPU or NVIDIA CUDA; detection + OCR for 80+ countries. Happy to answer questions about the pipeline. https://www.marearts.com/pages/marearts-anpr-sdk?utm_source=reddit&utm_medium=social&utm_campaign=w08_sdk40_story

Hello folks, Suva here from Hugging Face 🤗 and I'm back with something that might interest people working with finetuning.

3D Finetuning is generally not talked about enough and guides/resources on how to do it are scarce, so we tried to solve for it! We wrote a guide to how you can fine-tune the TRELLIS.2 model on your own tasks with LoRAs and also open-sourced the scripts and docker containers for it.

Blog Link: The Definitive Fine-Tuning Guide for 3D Generation Models

Happy hacking!

Hi everyone!

I've been working on an AI-powered road accident detection and monitoring system, and I'm excited to share my progress.

The goal is to explore how computer vision can help identify potential road accidents and improve incident monitoring, particularly in environments where road safety is a major concern.

What I've implemented so far:

• YOLO11 for object detection.

• Pose estimation for analysing human movement.

• BoT-SORT for object tracking.

• Accident-related event detection and monitoring.

• Near-miss and sudden-stop event monitoring.

• Telegram alerts for critical events.

• SMS notifications using Africa's Talking.

• A dashboard for monitoring video feeds and detected events.

I'm currently around 80% through development. I'm still working on improving detection reliability, reducing false positives and refining the alerting system.

This is an independent project I'm developing from Kenya 🇰🇪.

I'd genuinely appreciate feedback from people working in:

• Computer Vision

• AI/ML Engineering

• Object Tracking

• Intelligent Transportation Systems

My questions:

1. What would you improve in this system?

2. What additional features would make it more useful in real-world road monitoring?

3. Are there any approaches you'd recommend for improving accident detection reliability?

I'm happy to learn from your feedback and suggestions.

Thanks for taking the time to read about my project!

What kind of computer vision projects will it support?

The goal is to support multiple computer vision tasks through one application.

Currently working:

• Image Classification

• Object Detection

Planned:

• Image Segmentation

• Pose Estimation and Movement Tracking

• OCR (Text Recognition and Extraction)

• Image Processing and Enhancement

• Visual Question Answering and Image Captioning

The idea is that you shouldn't need to hire a developer or manually write hundreds of lines of Python code just to build a relatively small or medium-sized computer vision project.

You describe what you want to build, and the AI agent handles the development process, generates the code, trains the model using your local hardware, troubleshoots errors, and helps you get the final model ready for integration into your own application or system.

This is still an early experimental version, and I'm exploring whether this approach can actually make computer vision development more accessible.

I am planning to make this project with small one time fees for some features do you disagree with that and recommend that the app to be opensource instead and why ?

📢 Call for Papers – TPAD 2027 | IEEE ICASSP 2027

We invite submissions to TPAD 2027: Trustworthy Perception for Autonomous Driving – Adversarial Robustness, Multimodal Fusion, and Foundation Models, an official satellite workshop of IEEE ICASSP 2027, taking place on May 17, 2027, in Toronto, Canada.

The workshop brings together research on adversarial attacks and defenses, robust camera–LiDAR–radar fusion, secure foundation models, and safety-aware evaluation for autonomous driving.

📅 Important Dates

• Submission Deadline: December 2, 2026 (Extended)

• Acceptance Notification: December 16, 2026

• Workshop: May 17, 2027

📄 Accepted papers will be published in IEEE Xplore as part of the ICASSP 2027 workshop proceedings.

🔗 Workshop Website: https://tpad-2027.github.io/

📩 Contact: [email protected]

Full CFP and topics of interest are available in the attached image.

We welcome contributions from the signal processing, computer vision, machine learning, and autonomous driving communities!

I have a heavy interested in Augmented Reality since college. I'm currently a realtime systems software engineer and wondering if Computer Vision is the way to go if I want to get into the industry. Any Advice welcomed!

Thank you so much in advance!!!

Hey, I am a 4th year biomed engineering student (only recently shifted to computational from cell bio.) After looking on linkedin and handshake, I can’t really find any medical/bio CV internships. I have come across Leica and Canon (applied) but I doubt I’ll get it as my background is more so in cell bio and genomics.

Where should I be looking? What should I be searching for (terms/roles)? What should I do to beef up my resume / cater to CV experts?

My great-grandmother had Alzheimer's, and earlier this year I co-founded Memoria to build an in-home camera that watches an older adult for falls, bad nights and wandering, and alerts the family. That means following every person on every camera, all day. EdgeTAM, from Meta's SAM 2 family, tracked people best, but its code was written for editing one recorded video: a separate copy of the model per camera, and memory that grows about 12 MB every frame. For something families pay for monthly, that didn't work, so I built tamserve.

It does for video tracking what vLLM does for chat: one copy of the model, and every object on every camera goes through it as one batch. On one RTX 4090 with EdgeTAM:

• 64 cameras in 5.7 GB of GPU memory (321 frames/s between them). The original needs a copy of the model per camera, and 32 don't fit in 24 GB.

• Memory that doesn't grow: 0.28 GB for an object at frame 800, against 9.8 GB for the original.

• Faster: 219 vs 177 frames/s at 1 camera and 323 vs 280 at 8, against the original compiled and run as one process per camera.

• Live: six 720p cameras with nine objects, every camera at a full 30 frames/s (the video above).

• Same masks: 0.8685 vs 0.8686 on DAVIS 2017, and bit for bit on one camera with exact=True.

It also starts new objects on any frame, reconnects dropped cameras, and spreads cameras over several GPUs or machines. It runs EdgeTAM and all four SAM 2.1 sizes; with SAM 2.1 it saves memory but isn't faster, because that model keeps the GPU busy on its own.

pip install tamserve, or try it in Colab on a free T4. Apache-2.0.

• GitHub: https://github.com/aryamandarda/tamserve

• Colab: https://colab.research.google.com/github/aryamandarda/tamserve/blob/main/notebooks/quickstart.ipynb

If you find it useful, a star on GitHub helps other people find it. And if you're running tracking on live video, I'd love to hear what breaks.

Totally open source and free to use!

Ant Ling's new multimodal model is evaluated across several kinds of visual tasks. Three entries in its launch chart are:

- MathVision(w/CI): 96.80
- CharXiv_RQ: 85.15
- BabyVision: 69.85

These sit alongside its document, web-building and application-operation evaluations. Ling-3.2-flash accepts native image and video input through the documented API interfaces.

For anyone maintaining a visual-model evaluation set, how do you divide coverage between mathematical images, scientific charts and other visual questions? The release chart provides separate scores to compare against those different requirements.

Recently there has been lots of posts regarding the newer foundational models like LLM/VLMs and more traditional models.

So I thought it would be interesting to benchmark VLMs like Qwen3-VL-2B on more traditional vision tasks like classification and detection.

• Classification - surprisingly good: 93% top-1 accuracy, compared with 96% for Qwen3-VL-235B on the same samples. That makes the 2B model interesting for local classification.

• Attributes - acceptable, but uses far more tokens: 55.27% accuracy on OVAD, averaging 152 output tokens per sample versus 7 for classification—about 21× as many. Input token usage was also roughly 5× higher.

• Detection - poor: Only 6.75% recall. Additionally, 21% of samples failed to produce usable output, and 13% reached the token cap. I’d definitely keep an output limit in place.

• Grounding - also weak: 35% point-in-box accuracy and 22% box accuracy.

Tl;DR Classification was the strongest result here; reliable localization was much harder.

Benchmark results, methodology and code:
https://github.com/Tenkei/vlm-tasks/tree/main/benchmarks

I’m building a pipeline that turns broadcast highlights from the Tanzanian NBC Premier League into Metrica-style telestration: ground rings, a ball-carrier spotlight, passing lines with distances, and a tactical minimap. It works end to end, but I’m stuck on a few detection and tracking issues and would love advice.

Stack

• Compute: Modal (serverless GPUs, mostly A10G/L4); each ~10 s shot chunk runs in parallel with a 1 s warm-up.

• Shot filtering: histogram cut detection, then a shot is “wide” if the pitch-keypoint model finds ≥5 keypoints and ≥6 players. That’s 24 of 69 shots in a 14-minute highlights video.

• Detection: YOLO26x football model from Tactix (ball/GK/player/ref), batch 16, conf 0.15.

• Tracking: BoT-SORT + OSNet x0.25 ReID (boxmot). It replaced ByteTrack, which swapped IDs in clusters.

• Pitch mapping: roboflow/sports YOLOv8-pose keypoints (32 points), RANSAC homography, validation (inliers, condition number, jump rejection), One Euro filter on a projected quad, then offline Gaussian smoothing (±4 frames).

• Teams: median Lab colour of the torso centre, 2-cluster KMeans fit on the whole video. Per-frame reads with a ±0.5 s rolling vote, plus a “neither” class for refs.

• Rendering: HyperFrames (HTML/GSAP) to an RGBA PNG sequence. Ground layer composited under players with a grass-key matte, screen layer on top.

Fixes that helped

• Green kits read as referees: the detector labels Yanga players (green kit) as referees, probably because training data has refs in green. I promote any “referee” whose shirt colour matches a team.

• Split boxes: upper body and legs sometimes come as two stacked boxes, so I merge them.

• Off-pitch people: anyone whose feet map outside the pitch is dropped (ball boys, bench, stands).

• Marker jitter: zero-lag Gaussian smoothing of boxes over ±6 frames, possible because rendering is offline.

• What didn’t help: adding YOLO11x COCO person boxes on top. It failed on the same motion blur and added false positives.

Still struggling with

1. Far-side players (~20 px tall at 720p) often go undetected. Do I go higher-res inference / SAHI-style slicing, or fine-tune on a few hundred frames from this league?

2. Players on the ground in tackles are never detected. Any detector or trick that handles sprawled bodies?

3. Fast pans with motion blur: ReID helps carry tracks, but detections drop out. Is Deep-EIoU or another sports-specific tracker worth it over BoT-SORT?

4. Pitch calibration is valid on 74–98% of frames depending on the shot. Is PnLCalib or “No Bells Just Whistles” a big upgrade over keypoint-only homography on zoomed and partial views?

5. Team assignment: colour clustering works for red vs green, but would SigLIP embeddings (like roboflow/sports) be noticeably more robust under night lighting?

The footage is 720p broadcast highlights, mostly the main camera, with frequent replays and close-ups, which get filtered out. Happy to share the code or the lessons-learned doc if useful

Russian University Mathematics Dataset — 1,740 Problems with Handwritten and Typed Solutions

I am offering a unique, professionally prepared mathematics dataset for AI training, mathematical reasoning, handwritten mathematical OCR, and document understanding.

The dataset contains 1,740 Russian-language mathematics problems, ranging from school-leaving examination level to advanced university mathematics, with complete step-by-step solutions written by one human mathematics expert between 2010 and 2020.

Dataset overview

• 1,740 mathematical problems

• 3,450 original handwritten solution pages (covering 1,688 problems)

• 1,740 typed solutions in Microsoft Word and PDF

• 1,740 Markdown + LaTeX solutions

• 1,740 standalone LaTeX documents

• Approximately 50,603 mathematical formulas

• 927 figures, graphs and diagrams

• Approximately 4.2 million characters of typed mathematical content

• Total dataset size: approximately 2.2 GB

Mathematical coverage

The dataset covers 19 mathematical branches and 127 topics, including:

• Algebra and Linear Algebra

• Mathematical Analysis and Calculus

• Analytic and Differential Geometry

• Integrals and Field Theory

• Differential Equations

• Complex Analysis

• Mathematical Physics and PDEs

• Probability and Statistics

• Functional Analysis

• Topology

• Discrete Mathematics

• Numerical Methods

• Mathematical Economics

• Mathematical Olympiad Problems

• Russian State Examination (ЕГЭ)

Why is this dataset valuable?

1. Handwritten mathematical OCR

Original handwritten pages are paired with the author's corresponding typed solutions in LaTeX. This provides valuable supervision for training and evaluating models that convert handwritten mathematical documents into structured digital text.

2. Mathematical reasoning and LLM training

Every problem has a complete, step-by-step human-written solution. The material is suitable for supervised fine-tuning, mathematical reasoning evaluation, and answer verification.

3. Real handwritten mathematical documents

Unlike datasets containing isolated handwritten equations, this collection includes complete multi-page mathematical solutions, combining Cyrillic text, mathematical notation, formulas, and diagrams.

4. Human-authored content

All solutions were originally written by hand and subsequently transcribed by the same author between 2010 and 2020, before the emergence of LLM-generated content.

Available formats

Each problem has its own folder containing the available source materials:

• DOCX

• PDF

• Markdown with LaTeX formulas

• Standalone TEX

• JPEG / PNG handwritten pages

• PNG figures and diagrams

• Plain-text problem summaries

The dataset includes a structured manifest. JSONL, Parquet and Hugging Face-compatible packaging can be provided upon request.

Potential applications

• Handwritten mathematical OCR

• Handwritten equation recognition

• Image-to-LaTeX conversion

• Mathematical document understanding

• Multimodal model training

• LLM mathematical reasoning fine-tuning

• Mathematical benchmark development

• Figure and formula grounding

Sample available

A free evaluation sample containing 50 complete problems from all 19 mathematical branches, including 117 handwritten pages and approximately 1,600 formulas, is available for interested organizations and researchers.

Licensing and pricing

• Full dataset: USD 45,000

• Selected mathematical branches: approximately USD 26 per problem

• Free 50-problem sample for evaluation

A non-exclusive, worldwide, perpetual commercial AI-training and evaluation licence is offered. Commercial deployment and publication of trained model weights are permitted. Redistribution of the raw dataset is not permitted.

The problem statements originate from standard educational sources; no ownership of their original texts is claimed. The solutions and original handwriting are authored by the dataset creator.

https://huggingface.co/datasets/Tigran83/russian-university-math-handwriting

Contact

Tigran Grigoryan
Individual Entrepreneur — Armenia

Email: [email protected]

Serious inquiries from AI companies, OCR developers, research laboratories, universities and dataset buyers are welcome.

I can provide the detailed data card, sample package and licensing terms upon request.