my aim with this project is preparing a char OCR for Handwritten Geez that is CPU-first, cost-effective, and accurate

and i just finish testing it in my pc the model is cute tiny below 10mb make sense for char OCR - the video shows me testing it

thanks to its tiny size the response is 🪄 on cpu even though its not የዋዛ CPU - and as you have see the work is just starting i have to some haw get more data and boost the accuracy now it have many miss more than 19%

i will log more abut it as it progress

#YAP_C_OCR

This is the model + ደህና እደሩ

hi guys lets make naty rich 1000 followers https://t.me/mr_naty i want to enjoy his s.m.a. for context he is one of my fevorite content creators and a fluter dev.

😁 This happen to a demo in china

The man is using a motion capture suit mapped onto the Unitree G1. Every move from the human transfers straight to the robot in real time.

in China's this robot is being soled for about $18k.

from my view the demo is went well 😊 they achieved their objective to make the robot mimic the human move in Realtime with less sensors

https://deepmind.google/blog/rt-2-new-model-translates-vision-and-language-into-action/

TLDR; Google DeepMind's RT-2 is a groundbreaking vision-language-action (VLA) model. It builds upon large vision-language models, training them on both internet data and real-world robotic demonstrations. This fusion allows RT-2 to translate knowledge into robotic actions, enabling unprecedented generalization. The robot can interpret new commands, perform semantic reasoning, and operate on unseen objects and scenarios, demonstrating skills not present in its original training data. It marks a significant leap toward robots that can understand and act in our world with human-like semantic awareness.

Read more about it in the link

#Robotics #VLA

Note: Google DeepMind's RT-2 (Robotic Transformer 2) is a closed-source model.

BUT: its technical successor, OpenVLA, is fully open-source and specifically designed for consumer hardware. and i have attached the GitHub link below

https://github.com/openvla/openvla

#Robotics #VLA

🎉 Hi everyone! I introduced Test Time Augmentation (TTA) to the model testing tool. After testing with a slight trade-off in speed (from instant to a 1 sec delay), it is now 90% accurate in predicting any handwritten Amharic character.

Ladies and Gentlemen, I, am now successful in creating the first sub-10MB, microcontroller-friendly, CPU-first handwritten Geez OCR that is both cost-effective and accurate! 😊

I'm flying with joy right now! 🎉🎉🎉🎉

#AI #MachineLearning #Amharic #Geez #Tigrinya #OCR #DeepLearning #YAP_C_OCR

am preparing an exe file for everyone to use this testing tool

😊 I cant stress this enough isolated room is a must while testing your robot 🙄 am even inclined to add demo day in that list 😁

A gentle reminder that there is no perfect/predefined age to take the holy communion. Repent and claim your eternal life.

#Geez_OCR #Update I just finished preparing the standalone file and since it have to pack the entire tensorflow, opencv, PIL, and numpy library 😅 it is big above 700MB if you machine is a window give it a try

and DM me about your experience here is my gift after 3 sleepless night

i believe among my next steep is

1, to write a paper on this specially I have to document the new efficiency gain and accuracy with this project

2, prepare a web version so any one from any machine can try and test the model even from phone

3, expand this project to word OCR with similar objective of CPU first, Fast and Accurate model with limited resource

4, log the crazy tricks i used in this project here

Note: this should work fine even if your pc have 4GB ram only 👆 in the previous log

you can press the red X button to tell it when it predicted wrong and it will record it for future use if you export and share the sql database of your handwriting to share just press the green sql button and it will give you sql dataset of what you have tested so far and you can email me that.

Good Night 🫶