Best Speech Recognition Software in 2026: Free and Paid Compared
Feb 23, 2026 · This guide provides a comprehensive comparison of the best speech recognition software available in 2026, breaking down their strengths, weaknesses, and ideal use cases.
Distillation of Human-Object Interaction Contexts for Action Recognition
Modeling spatial-temporal relations is imperative for recognizing human actions, especially when a human is interacting with objects, while multiple objects appear around the human differently over time. Most existing action recognition models focus...
Probabilistic Event Calculus for Event Recognition
Symbolic event recognition systems have been successfully applied to a variety of application domains, extracting useful information in the form of events, allowing experts or other systems to monitor and respond when significant events are recognise...
Human-Centered Emotion Recognition in Animated GIFs
As an intuitive way of expression emotion, the animated Graphical Interchange Format (GIF) images have been widely used on social media. Most previous studies on automated GIF emotion recognition fail to effectively utilize GIF's unique properties, a...
Asian Stamps Identification and Classification System
In this paper, we address the problem of stamp recognition. The goal is to classify a given stamp to a certain country and also identify the year it is published. We propose a new approach for stamp recognition based on describing a given stamp image...
Text Detection Forgot About Document OCR
Detection and recognition of text from scans and other images, commonly denoted as Optical Character Recognition (OCR), is a widely used form of automated document processing with a number of methods available. Yet OCR systems still do not achieve 10...
Saliency-Aware Automatic Buddhas Statue Recognition
Buddha statues, as a symbol of many religions, have significant cultural implications that are crucial for understanding the culture and history of different regions, and the recognition of Buddha statues is therefore the pivotal link in the field of...
The Influence of Iconicity in Transfer Learning for Sign Language Recognition
Most sign language recognition research relies on Transfer Learning (TL) from vision-based datasets such as ImageNet. Some extend this to alternatively available language datasets, often focusing on signs with cross-linguistic similarities. This body...
Generalization and Network Design Strategies - Yann LeCun
Building a constrained network for image recognition appears to be a feasible task. We describe a small handwritten digit recognition problem and show that, even though the problem is linearly separable, …
Support Vector Machine (SVM) Recognition Approach adapted to Individual and Touching Moths…
This paper aims at developing an automatic algorithm for moth recognition from trap images in real-world conditions. This method uses our previous work for detection [1] and introduces an adapted classification step. More precisely, SVM classifier is...
An Evaluation of Large Pre-Trained Models for Gesture Recognition using Synthetic Videos
In this work, we explore the possibility of using synthetically generated data for video-based gesture recognition with large pre-trained models. We consider whether these models have sufficiently robust and expressive representation spaces to enable...
Image Recognition of Tea Leaf Diseases Based on Convolutional Neural Network
In order to identify and prevent tea leaf diseases effectively, convolution neural network (CNN) was used to realize the image recognition of tea disease leaves. Firstly, image segmentation and data enhancement are used to preprocess the images, and...
AU-Guided Unsupervised Domain Adaptive Facial Expression Recognition
The domain diversities including inconsistent annotation and varied image collection conditions inevitably exist among different facial expression recognition (FER) datasets, which pose an evident challenge for adapting the FER model trained on one d...
Sensor Data Augmentation from Skeleton Pose Sequences for Improving Human Activity Recogni…
The proliferation of deep learning has significantly advanced various fields, yet Human Activity Recognition (HAR) has not fully capitalized on these developments, primarily due to the scarcity of labeled datasets. Despite the integration of advanced...
Human Activity Recognition using Attribute-Based Neural Networks and Context Information
We consider human activity recognition (HAR) from wearable sensor data in manual-work processes, like warehouse order-picking. Such structured domains can often be partitioned into distinct process steps, e.g., packaging or transporting. Each process...
Transformer-based Models to Deal with Heterogeneous Environments in Human Activity Recogni…
Human Activity Recognition (HAR) on mobile devices has been demonstrated to be possible using neural models trained on data collected from the device's inertial measurement units. These models have used Convolutional Neural Networks (CNNs), Long Shor...
Multi-blank Transducers for Speech Recognition
This paper proposes a modification to RNN-Transducer (RNN-T) models for automatic speech recognition (ASR). In standard RNN-T, the emission of a blank symbol consumes exactly one input frame; in our proposed method, we introduce additional blank symb...
BloomNet: Exploring Single vs. Multiple Object Annotation for Flower Recognition Using YOL…
Precise localization and recognition of flowers are crucial for advancing automated agriculture, particularly in plant phenotyping, crop estimation, and yield monitoring. This paper benchmarks several YOLO architectures such as YOLOv5s, YOLOv8n/s/m,...
A Benchmark Comparison of Visual Place Recognition Techniques for Resource-Constrained Emb…
Visual Place Recognition (VPR) has been a subject of significant research over the last 15 to 20 years. VPR is a fundamental task for autonomous navigation as it enables self-localization within an environment. Although robots are often equipped with...
Low-Resolution Action Recognition for Tiny Actions Challenge
Tiny Actions Challenge focuses on understanding human activities in real-world surveillance. Basically, there are two main difficulties for activity recognition in this scenario. First, human activities are often recorded at a distance, and appear in...