人脸识别技术依赖于图像标注来准确检测图像或视频中的人脸特征。这涉及到使用地标检测或关键点标注来标记眼睛、鼻子、耳朵和嘴巴等面部特征。为了使模型表现良好，需要多样化和准确的训练数据。图像标注在自动驾驶汽车、医学影像和诊断、激光雷达测绘、多模态模型训练等领域都有应用。高质量的图像标注通常在专家的监督下进行，使用各种图像标注工具来标记图像中的有用信息，例如光学字符识别（OCR），用于从图像中检测和提取文本。

👁️‍🗨️人脸识别技术依赖于图像标注，通过地标检测或关键点标注来标记面部特征，是计算机视觉模型处理和检测面部特征的基础。

🚗图像标注的应用广泛，不仅限于人脸识别，还包括自动驾驶汽车、医学影像、激光雷达测绘以及多模态模型训练等多个领域，这些领域都依赖高质量的图像标注数据来训练模型。

🧰图像标注包括2D和3D两种类型，2D标注使用边界框、分割、地标检测等方法，而3D标注则处理点云数据，考虑深度、距离和体积等因素，以满足不同场景的需求。

🧾光学字符识别（OCR）是图像标注的一个重要任务，用于从图像中检测和提取文本，应用于文档数字化、车牌识别、自动收费系统和零售收据扫描等场景。

Facial recognition technology (FRT) relies on image annotation to accurately detect human facial features in images or videos. This involves using landmark detection or keypoint annotations to label facial traits, such as the eyes, nose, ear, and mouth. Without accuracy in training data, computer vision models cannot process and detect facial features. This is similar to a person trying to learn from a textbook in an unfamiliar language that has all the information but is unable to understand the context.

Much like facial recognition, image annotation has several use cases in fields such as autonomous vehicles, medical imagery and diagnostics, LiDAR mapping, multi-modal model training, and others.

For models to perform well, diverse and accurate training data is needed. To understand the rationale behind this, we need to examine the types of image data annotations, their usefulness, and the diverse tasks associated with quality image annotation.

Types of Data Used with Image Annotation

As part of training data, Cogito Tech offers images, and videos to train machine learning models.

3D image/video annotation: This is performed on three-dimensional data, such as point clouds, to factor depth, distance, and volume into account using advanced imaging tools.

2D image/video annotation: This is performed on two-dimensional data, and some methods include bounding boxes, segmentation, landmark detection, polylines & key points.

Understanding Image Annotation Tasks

To keep quality levels high, image annotation is usually done under the supervision of an expert, using various image annotation tools to label useful information in images.

Optical Character Recognition (OCR)
Our purpose is to detect and extract text from images for tasks like document digitization for extracting text from scanned files. It also involves tasks like license plate recognition and automated toll systems and in retail for scanning receipts. Our annotators are skilled in using bounding boxes or polygon annotations required for OCR.

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