Globose Technology Solutions

Video Transcription services with GTS.AI Basic insight on Transcription (Video transcription) Transcription is the process of converting spoken language into a written or electronic text document. Video transcription involves creating a written record of the spoken content in a video, which can include dialogue, narration or other audio elements. Video transcription can be useful in a variety of contexts, including: Accessibility: Providing a text version of a video’s audio content can make it accessible to individuals who are deaf or hard of hearing. SEO: Including a text transcription on a webpage can help search engines index the content more effectively, making it more discoverable in search results. Language translation: A transcription can serve as a source text for translation into other languages. Content Creation: Transcribing a video can be a helpful tool for content creators who want to repurpose the videos content does for other uses, such as blog posts or articles.  Ov

WHY ARTIFICIAL INTELLIGENCE IS INCOMPLETE WITHOUT DATA ANNOTATIONS Introduction Data annotations in simple words can be described as sets of data given to machines to understand what the specific data is. The labelling or tagging of information on datasets provided for the machine learning models to let it understand what the data sets are. Data sets come in formats such as text, audio, video, and image. The data annotators at Globose Technology Solutions tag different elements on the information provided in the various formats so that the machine retains the data processes it then prepares itself for any enhancements required in the future based on the existing information provided.  Data Annotation Services  is a time-consuming process. It takes several hours to feed the data bit by bit, pixel by pixel into the system precisely. The data annotators ensure to deliver the best and most accurate result as the best data collectors provide the exact information required. Audio annotations

Extracting Audio Transcription for machine learning Introduction Firstly let me just brief you about audio transcription. Extracting audio transcription for machine learning can be a useful task in a variety of applications, such as speech recognition, language translation, and natural language processing. In other words audio transcription can be defined as the process of converting speech in an audio file into written text. Similarly AI transcription uses AI technology to convert human speech into text. Extracting Audio Transcription for machine learning involves converting data in audio format to text formats for training machine learning models. Audio transcription involves the basic steps: Preprocessing: Preprocessing is the process of cleaning the given data, making sure there are no background noises, and ensuring it gives a consistent performance throughout the audio file. Speech recognition: Convert the audio files into text using automatic speech recognition (ASR) software

For what reason is Image Annotation Services are Significant? Introduction While making hearty datasets for PC vision, many elements are fundamental to consider, for example, information inclination, source quality, project extension, and test amount. We'll cover these points in later posts. In any case, until further notice, we should zero in on a few normal information explanation issues that might influence PC vision projects. Drawing labels inaccurately around objects: While marking bouncing boxes and polygons, it's critical to define the boundaries right external the article, yet not excessively far outside and not inside the layout. Not labels all article examples: In the event that an occasion of an item type isn't named while Data Annotation Services , the AI model may not get familiar with the right examples required for discovery. Unclear or no labels guidelines: It's fundamental to have exact information comment guidelines for each venture, particularly while

Maximizing Machine Learning Accuracy with High-Quality Audio Transcription Introduction No mystery voice acknowledgment has progressed fundamentally since IBM presented its most memorable discourse acknowledgment machine in 1962. With voice-driven applications like Amazon's Alexa, Apple's Siri, Microsoft's Cortana, and many voice-responsive elements of Google, voice acknowledgment has become progressively implanted in our regular routines as innovation has advanced. Each new voice-intelligent gadget we bring into our lives, from telephones to PCs to watches to coolers, builds our dependence on man-made consciousness (computer based intelligence) and AI. Computerized reasoning is one troublesome innovation that has adjusted how significant information is taken care of. While working with huge analyzable arrangements of information, for example, text, AI is believed to be at its ideal. In any case, most accessible information isn't in text structure since it is likewise i

Invoice dataset collection for machine learning mining process Introduction Invoices for telecom companies are the most extensive and most difficult invoices from any industry because of the complicated telecom contract, product, and billing procedures. Anomalies and billing errors are not uncommon as a result. Anomalies are a frequent issue across many sectors and including the telecom sector. It can find telecom anomalies everywhere in various Telecom processes that will connect to security breaches, the performance of the network, or even fraud. Recently, the use of AI to address these difficulties has increased. The telecom invoice is among the most complex invoices made in every industry. There are always errors due to the enormous variety and number of services and products that can offer. The products will compromise on specific product characteristics, and the sheer quantity of these features differentiates a product and the many combinations that create the diversity. Then the

How important is Outsourcing DATASET FOR MACHINE LEARNING Introduction Outsourcing data sets for machine learning can be quite important, as the quality and diversity of the data used to train machine learning models can greatly impact their accuracy and overall performance. A high-quality and diverse data set can help the model better learn and generalize patterns in the data, leading to improved results when making predictions on new, unseen data. On the other hand, if the data set used for training is limited in quality or diversity, the model may not be able to accurately capture the underlying patterns in the data, leading to poor performance and incorrect predictions. However, it's also important to be cautious when outsourcing data sets for machine learning, as the data may contain sensitive information, or the vendor may not have obtained the data in an ethical or legal manner. It's crucial to thoroughly vet the vendor and ensure that the data was obtained ethically and

Evaluate the Quality of a Video Dataset for Machine Learning Introduction A video dataset is a collection of video clips and related information that is used for training and testing machine learning models for video analysis tasks. The video clips in a video dataset can be broken down into individual frames or images, which are used as input to train machine learning models for tasks such as action recognition, object detection, and video captioning. The quality and diversity of the video dataset are critical for the performance of the machine learning models. A video dataset should be diverse and representative of the target domain, with a sufficient number of samples for each class. The video clips should also be of high quality, with adequate resolution, lighting, and frames per second (fps). Annotated video datasets, where each video clip is labeled with information such as action classes, object classes, and captions, are particularly useful for training supervised machine learni

Everything You Should Know About Data Annotation Services What is Computer Vision computer vision is a branch in artificial intelligence (AI) which allows computers and systems to extract useful information from videos, digital images and other inputs from the visual sphere -taking actions or offer recommendations based upon the information. If AI allows computers to consider, computers' computer vision can help them to look, feel, and be able to comprehend. Like a human being can simply look at an object to be able to comprehend the nature of it and what it is like it is possible for a computer to perform the same, provided that it is properly trained to be able to do it. Humans benefit from having lifetimes of context, which allows them to learn to distinguish objects and how far apart they are, if they're moving, and whether there is anything wrong with an image. Computer vision also performs the same tasks, however, it does so using cameras and data. Computer vision systems

Optical Character Recognition For Business Owners Introduction OCR is also known as text recognition. Machine-learning-based OCR allows you to extract printed and handwritten text from images like posters, street signs, product labels and reports. The text can be extracted in a variety of formats, including words, text lines and paragraphs. Digital versions of the scanned text are also available. This significantly reduces the amount of data that must be entered manually. How does OCR relate to Intelligent Document Processing? Intelligent Document Processing (IDP), uses OCR Training Dataset , relationships and key-values. It also provides other insights through advanced machine-learning-based AI services like . Form Recognizer is a document-optimized Reader OCR engine that can be delegated to other models for better-end insights. Form Recognition allows you to extract text from scanned or digital documents. OCR engine Microsoft's OCR engine supports multiple advanced machine learni

Traffic Analysis in the Original Traffic Video Dataset Introduction The rapid expansion of urban population as well as motor vehicles has created a variety of traffic issues. Intelligent transport technology (ITSs) are considered to be the best solution to these issues. Due to the advancement of Internet of Things (iot) technology, communication technology, and computer vision, surveillance of traffic is now a key technology of collecting traffic parameters and plays an important role. Traffic flow is a element of ITS, and quickly and accurately detecting and counting vehicles using traffic videos is a popular research subject. In the past few decades several vision techniques have been developed to count vehicles automatically in traffic video. A lot of the existing methods for counting vehicles are based on a vehicle detector in relation to the appearance of the car and other features. These are detected by foreground detection. Moreover, vehicles are counted according to the results