Annotation Tools

Upgrade the scaling up backbone of your data labeling projects

Project types we serve

Our services include but are not limited to image & video annotation, audio annotation, text annotation, and LiDAR annotation, all combined to cover 16 types of data labeling projects to cater to all your scaling needs
Train and develop the best computer vision algorithms with pixel-perfect and frame-by-frame-excellence Annotation Tools. TagOn image & video annotation services support a vast range of cases, from cancer detection to self-driving vehicles to many more.
  • project-types
    Adding meaningful information/labels to a massive quantity of images to speed up training machine learning.
  • Choosing platform is important to a image annotation project
    Precisely identify the objects depicted in the photos across the dataset for machines to distinguish objects, facial expressions, landscapes, etc. It can pinpoint either a single object or more, then label them accurately.
    Object detection is especially effective when applied for greater data volume, such as in MRI (Magnetic Resonance Imaging) scans with multi-frame data to recognize characteristics and monitor how those change over time.
  • Segmenting the entire photo and identifying displayed objects to item level, presented with similar grouped areas & objects. Image segmentation includes 2 sub-types:
    Semantic segmentation: Segment out all the related pixels into a class. This is used for annotation that does not need counting or tracking objects over several images
    Instance segmentation: Delineate each object separately without putting it into any classes. This can segregate overlapped or very similar objects based on their borders, suitable for identifying the present, position, number, size, and shape of objects.
  • Optical Character Recognition (OCR) - A type of image annotation project
    Extract text from printed or handwritten text characters in digital pictures of physical documents. With our global-coverage annotators in 20+ countries, TagOn ensures OCR operation in 50+ languages & dialects.
    Our system supports a variety of document formats, from bar codes to hand-printed text (ICR), checkboxes (OMR), etc
  • Accurately track temporal and spatial objects’ movement with frame-by-frame video annotation services. Our solution is the combination of both human annotators and automated annotation tools to save project time and costs, especially when it comes to complicated video annotation. Object tracking is extremely helpful when in the deployment of manufacturing robots, autonomous technologies, etc
Powering up the development of your chatbots, virtual assistants, text to speech, and other natural language processing (NLP) technology with our novel audio annotation tools and a large crowd of annotators around the world.
  • Listening and analyzing audio recordings to classify audio based on languages, dialects, contents, semantics, and other factors. TagOn takes pride in our wide-coverage and high-skill annotators to ensure tailoring any of your unique classification requests for each labeling project.
  • Comprehensively divide your audio recording stream, which frequently contains several speakers (possibly overlapping), into homogenous segments to label your audio detailedly and correspondingly to individual speakers. Achieving the greatest accuracy with our fully-supervised speaker diarization services by securing fewer mistakes in the final transcribing and deciding frameworks to use as a mathematical formula.
  • We offer a hyper-localized audio transcription solution to capture any audio content. Our high-skilled annotators ensure great dealing with complex accents, and dialects of 20+ countries with thousands of geography-specification. In addition, by combining both AI-powered tools and human annotators, we help optimize time and cost while maintaining a high transcription accuracy rate.
Listening and analyzing audio recordings to classify audio based on languages, dialects, contents, semantics, and other factors. TagOn takes pride in our wide-coverage and high-skill annotators to ensure tailoring any of your unique classification requests for each labeling project.
  • Profoundly categorize documents into relevant meaningful categories or classes. Our annotators will tag appropriate classes to your documents from a set of predefined labels to help simplify and streamline the process of organizing and keeping documents and data. Managing text and huge amounts of unstructured information for overwhelming data projects is no longer an issue.
  • Employing skilled annotators to precisely identify and categorize key information - entities - in the text. After detected, entities will be classified accurately into predetermined categories. Not only do we guarantee best-in-class labeling qualifications but we also serve a wide range of projects, from human resources to content classification to search and recommendation technologies, etc
  • Carefully select annotators based on requestors’ specific demands, we generate answers by querying a knowledge base (a structured database of information) or an unstructured collection of natural language documents. Our question answering service addresses a wide range of question categories, such as facts, lists, definitions, hypothetical, semantically limited, and cross-lingual inquiries.
  • Carefully condense enormous amounts of information into short to produce concise and fluent summaries while retaining the meaning of the original text document. Furthermore, TagOn annotators can satisfy advanced skills critical to high-requirement summarization, such as paraphrasing, generalization, and the inclusion of real-world information.
Tackle one of the most complex problems of computer vision: LiDAR point cloud data with top-of-the-class tools, assuring accuracy to point cloud level of more than 100K points per task.
  • Delivering precise object identification for LiDAR point cloud data with skilled annotators capturing the sizes, locations, and directions of target objects. Quickly obtain the real-world practice for training data by applying LiDAR coordinate frame and oriented 3d bounding boxes
  • Point-by-point segmentation of 3D point clouds to segregate 3D objects and areas such as buildings, automobiles, people, background, etc. Optimizing the process by using the coarse-to-fine segmentation structure to access the entire point clouds as a single input and assign each point to a class.
  • Precisely object recognition and tracking frame-by-frame in 3D point cloud settings. Our services enable tracking multiple objects by providing unique object IDs with advanced computing capacity. Along with TagOn novel 3D bounding boxes, making use of 6 degrees of freedom with pitch, roll, and yaw angles helps deliver the best for your project.

Tools we provide

Our services include but are not limited to image & video annotation, audio annotation, text annotation, lidar annotation, all combined to cover 16 types of data labeling projects to cater to all your scaling needs
  • provide-content
    Image Video

    Rectangles/2D Bounding Boxes

    When they should be used: Opted for situations when the object doesn’t fit well in a rectangle due to its irregular shape (such as bodies of waters, land areas, etc), size or the direction of the image or developers need a more precise annotation


    Extremely handful for projects with complex irregularly shaped objects

    Create pixel-perfect annotations


    Taking a considerably longer time to annotate than bounding box tools

    Too much point can create lagging when computing

  • Image Video


    Using the rectangle to enclose the target object in the image or video. The box should be as near to every edge of the item as possible.

    When they should be used: When the item’s shape isn’t important or when occlusion isn’t a problem.


    Simple and quick to draw, easily save time for enormous data projects

    One of the most common and cheapest annotation tools to compute


    Only restricted for rectangles and squares

    Cannot precisely only the objects’ pixels but rather includes backgrounds, other entities’ parts, etc

    Bad for composite and occluded objects.

  • Image

    Key Points

    Consisting of a series of points (key points), connected by lines. TagOn also provides pre-developed keypoint frame for common projects such as skeleton, facial expressions, etc

    When they should be used: Useful for movement tracking and prediction, keeping track of differences between objects that always have the same structure (e.g. human figures and facial features). Therefore they are commonly used in face recognition, recognizing body parts, postures, and facial emotions.


    Quick and simple when using our pre-developed keypoint frames: annotators only need to align the point to objects

    Ensuring that no node in the sequence is overlooked.


    Only useful for objects with the same regular defined structures

    Might cause confusion for annotators when the nodes of the objects are not visible or hidden

  • Image

    3D Cuboids

    3d bounding boxes encapsulate the objects and display them with depth, allowing computer vision algorithms to interpret volume and orientation. For annotators, drawing cuboids means placing 2 rectangles (front and back) and 2 shapes will automatically connect to create a 3D cube.

    When it should be used: Used when depth perception is critical and can be applied in a vast range of industries: 3D structure detection in Geospatial, Lidar point cloud data for vehicle tracking, analyzing CTs and MRIs in medical, etc


    Providing extra information about the depth of the object rather than just length and width in 2D bounding boxes.


    Time-consuming, require more skills to draw and more computing capacity to process a large amount of data

  • Audio

    Audio Segmentation

    Dividing audio recordings into small pieces to easily annotate data at the highest accuracy

  • Text

    Text Segmentation

    Easily highlight document components (keywords, phrases, or sentences) with including tags to add meaning to text data

  • Annotation Tools
    Supporting tools


    Capitalizing pre-trained tools to annotate your complex images and videos at speed. Ensuring precision with highly-detailed auto edge-segmentation to pixel levels.

  • Supporting tools


    Calculate distances in the image in pixels to define objects features and tailor to requestors’ demands

  • Data Annotation
    Supporting tools

    Pathfinder - Unite

    Create a compound shape by combining all the selected shapes into a single larger one

  • Supporting tools

    Pathfinder - Minus (front or back)

    Allowing you to use any top/back object to create a cutout from the one underneath/above. This option is used to subtract areas of an illustration by making adjustments to the stacking order