Our Services

What Solutions We Provide

Bounding box annotation

An item or a group of points is contained within the bounding box, an abstract rectangular container. The bounding box is a term used in digital image processing to describe the border coordinates around an image. They frequently serve as a reference point for object recognition, bind or identify targets, and generate collision boxes for those targets.

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Semantic segmentation

With pixel-level accuracy, the semantic segmentation and annotation aids in the identification and classification of the items. It is used to segment and annotate objects in one of the available classes so that machine learns more accuratelyand classify them. For the creation of semantic segmentation datasets for deep machine learning and AI models in the context of visual perception, Data Annotation provides a very effective platform.

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Line annotation

The training model for autonomous driving needs pixel-level annotation. According to the Project demands we offers line annotation services with best tools and structured line annotation data. In addition, we also use cutting-edge annotation tools to ensure no detail is missed. Since our team is aware that from experience, impose crucial efforts while processing data annotations, we guarantee complete accuracy.

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Polygonal annotation

Polygon annotation is used to mark irregular shapes or coarse things so that machines may recognise them quickly. To create polygon annotated datasets for machine learning models we are empowered with the human image annotation service. Aerial imagery from drones or satellite photos such as house rooftops, chimneys, pools and trees etc can be easily annotated using this method.

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Video Annotation

Computers / Machines / Robots envisage the moving objects in the natural environment through labelled objects in videos. Each object of interest in the video is marked up frame-on-frame by tweaking the algorithms so that machines can recognize it. For the purpose of producing the best computer vision training data, Data Annotate offers a video annotation solution for audiovisual files also.

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3D Cuboid Annotation

Human beings are 3D creatures. The human eyes perceive the objects in 2D only and send it to retina. The human brain combines two 2D images and extrapolate the depth of object to sense the 3D objects called Stereoscopic vision. Spatial objects are in 3D which has different lengths, heights and widths. But scientifically the real-world things are multidimensional that cannot be sensed by human eyes.

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3D Point Cloud Annotation

For autonomous cars, the most important sensor operates at higher autonomy levels (L4-L5). Deep learning algorithms needstons of trained data that can be annotated using point clouds. At some instances, due to the low resolutionand length of the process, intricate annotation procedures are not sufficient. Beyond this,annotating with the 3D point cloud data is most convenient.

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Audio Annotation

Audio data in any format is understandable by machines. Annotation of audios are necessary for Natural Language Processing (NLP) based speech recognition models. This improves the audibility and readability of chatbots and other virtual assistants. Mei Infotech offers audio annotation services that add metadata to the recorded sounds or speech to enhance the human-bot interaction.

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Landmark Annotation

The technique of landmarking is mapping a series of points to provide precise information that can identify the various sizes and shapes of items.For AI machine vision, in-depth and insight knowledge is gained by Landmark Point Annotation which identifies human faces, gestures, facial emotions, and postures. In order to construct the datasets, Data Annotate uses a landmark point annotation service to make a human face and various stances recognizable.

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