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Education

AI addresses some of the biggest challenges in Education vertical today by innovating teaching & learning practices. The aim of using AI in education is to enhance the learning experience, improve the effectiveness of instruction & provide learners with more personalised & efficient education.

Educational Content Tagging

In Educational Content Tagging, our team systematically labels educational resources with metadata for enhanced organisation & accessibility within digital learning platforms. This helps streamlines navigation for students, enabling them to find relevant materials quickly, facilitating a personalised learning experience, benefiting both students & educators in their quest for knowledge dissemination.

Handwriting Recognition

For Handwriting Recognition services, our team performs bounding box annotation wherein we annotate handwritten text in images & documents for text recognition. Each character & word are meticulously marked, with classes including letters, numbers, symbols & mathematical formulas. This data aids in developing handwriting recognition systems for tasks like digitization, translation, or text analysis.

Image Annotation for Educational Apps

In Image Annotation for Educational Apps, the objective is to annotate images which then helps train the App's algorithms to recognise & classify educational content accurately. This involves labelling objects, text, concepts & other relevant attributes within the images.

Speech-to-Text Annotation

In Speech-to-Text Annotation, our team creates audio transcription by converting spoken audio into written text to interpret & process spoken language. This includes manually transcribing spoken content, including words, punctuation & speaker identification. Additional classes like timestamps, speaker labels & linguistic features enhance machine understanding.

Question-Answer Pair Annotation

In Question-Answer Pair Annotation, our team creates bounding boxes & extracting question / answer pairs from publicly available test-prep & study guide eBooks. This involves a systematic process of identifying text regions corresponding to questions & answers within the digital documents. Bounding boxes are then used to extract the text content for each question & its corresponding answer, thereby transforming unstructured textual data into structured data.

Interactive Learning Data Annotation

In Interactive Learning Data Annotation, our team annotates data to train the platform's algorithms to recognise & understand spoken language. This involves labelling audio data, transcribing spoken text & annotating attributes related to language proficiency, accents, emotions & contextual understanding.

Language Translation Training Data

For Language Translation Training Data, our team performs text pair annotation in multiple languages involving annotating linguistic features like grammar, syntax & vocabulary usage to train machine translation models. This annotated dataset serves as training material for machine learning algorithms to develop accurate translation systems tailored to context.

Semantic Segmentation for Educational Videos

For Semantic Segmentation for Educational Videos, our team performs bounding box, segmentation & pixel-wise masking. We also mark objects, actions & concepts relevant to educational content. Each pixel in the extracted frames is labelled with corresponding class labels.

Student Engagement Data Annotation

For Student Engagement Data Annotation, our team perform annotation on textbooks, lecture slides, classroom videos & online platforms. We identify participation, interaction, collaboration & attention levels. This aids educators in enhancing student engagement.