# Gesture Recognition Based on TFLite

DevFeed: [Gesture Recognition Based on TFLite](<https://devfeed.tech/articles/gesture-recognition-based-on-tflite-13765.md>)

Original publisher: [Read original article](<https://developer.espressif.com/blog/2026/04/gesture-recognition-based-on-tflite/>)

Author: John Lee

Published: 2026-04-10T00:00:00Z

Content type: tutorial

Language: en

Sources: [Blog on Developer Portal](<https://devfeed.tech/sources/blog-on-developer-portal.md>)

Topics: [TensorFlow Lite](<https://devfeed.tech/topics/tensorflow-lite.md>), [Espressif](<https://devfeed.tech/topics/espressif.md>), [model-deployment](<https://devfeed.tech/topics/model-deployment.md>), [Tensorflow](<https://devfeed.tech/topics/tensorflow.md>), [Keras](<https://devfeed.tech/topics/keras.md>), [On-device AI](<https://devfeed.tech/topics/on-device-ai.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [blog](<https://devfeed.tech/tags/blog.md>), [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [development-board](<https://devfeed.tech/tags/development-board.md>), [edge-ai](<https://devfeed.tech/tags/edge-ai.md>), [edge-computing](<https://devfeed.tech/tags/edge-computing.md>), [esp-idf](<https://devfeed.tech/tags/esp-idf.md>), [espressif](<https://devfeed.tech/tags/espressif.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [inference](<https://devfeed.tech/tags/inference.md>), [keras](<https://devfeed.tech/tags/keras.md>), [model-deployment](<https://devfeed.tech/tags/model-deployment.md>), [model-training](<https://devfeed.tech/tags/model-training.md>), [tensorflow-lite](<https://devfeed.tech/tags/tensorflow-lite.md>)

## AI overview

This tutorial demonstrates gesture recognition on Espressif SoCs using TensorFlow Lite Micro. It covers data collection, model training, conversion for TFLite Micro, and deployment with C++ code for model loading, preprocessing, and inference.

## Source excerpt

This article demonstrates how to implement gesture recognition using TensorFlow Lite Micro on Espressif SoCs. It covers the complete workflow from data collection and model training to model deployment, showcasing TensorFlow Lite Micro's applications in edge AI.