An inclusive review on deep learning techniques and their scope in handwriting recognition

• URL: http://arxiv.org/abs/2404.08011v1
• Date: Wed, 10 Apr 2024 06:30:33 GMT
• Title: An inclusive review on deep learning techniques and their scope in handwriting recognition
• Authors: Sukhdeep Singh, Sudhir Rohilla, Anuj Sharma,
• Abstract summary: Deep learning expresses a category of machine learning algorithms that have the capability to combine raw inputs into intermediate features layers. Deep learning has particularly witnessed for a great achievement of human level performance across a number of domains in computer vision and pattern recognition. This paper presents a survey on the existing studies of deep learning in handwriting recognition field.
• Score: 4.318047857743103
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Deep learning expresses a category of machine learning algorithms that have the capability to combine raw inputs into intermediate features layers. These deep learning algorithms have demonstrated great results in different fields. Deep learning has particularly witnessed for a great achievement of human level performance across a number of domains in computer vision and pattern recognition. For the achievement of state-of-the-art performances in diverse domains, the deep learning used different architectures and these architectures used activation functions to perform various computations between hidden and output layers of any architecture. This paper presents a survey on the existing studies of deep learning in handwriting recognition field. Even though the recent progress indicates that the deep learning methods has provided valuable means for speeding up or proving accurate results in handwriting recognition, but following from the extensive literature survey, the present study finds that the deep learning has yet to revolutionize more and has to resolve many of the most pressing challenges in this field, but promising advances have been made on the prior state of the art. Additionally, an inadequate availability of labelled data to train presents problems in this domain. Nevertheless, the present handwriting recognition survey foresees deep learning enabling changes at both bench and bedside with the potential to transform several domains as image processing, speech recognition, computer vision, machine translation, robotics and control, medical imaging, medical information processing, bio-informatics, natural language processing, cyber security, and many others.

Related papers

• Deep Learning for Educational Data Science [0.6138671548064356]
  Use cases range from advanced knowledge tracing models that can leverage open-ended student essays or snippets of code to automatic affect and behavior detectors. This chapter provides a brief introduction to deep learning, describes some of its advantages and limitations, presents a survey of its many uses in education, and discusses how it may further come to shape the field of educational data science.
  arXiv  Detail & Related papers  (2024-04-12T19:17:14Z)
• Deepfake Generation and Detection: A Benchmark and Survey [134.19054491600832]
  Deepfake is a technology dedicated to creating highly realistic facial images and videos under specific conditions. This survey comprehensively reviews the latest developments in deepfake generation and detection. We focus on researching four representative deepfake fields: face swapping, face reenactment, talking face generation, and facial attribute editing.
  arXiv  Detail & Related papers  (2024-03-26T17:12:34Z)
• Vision+X: A Survey on Multimodal Learning in the Light of Data [64.03266872103835]
  multimodal machine learning that incorporates data from various sources has become an increasingly popular research area. We analyze the commonness and uniqueness of each data format mainly ranging from vision, audio, text, and motions. We investigate the existing literature on multimodal learning from both the representation learning and downstream application levels.
  arXiv  Detail & Related papers  (2022-10-05T13:14:57Z)
• Emotion Recognition In Persian Speech Using Deep Neural Networks [0.0]
  Speech Emotion Recognition (SER) is of great importance in Human-Computer Interaction (HCI) In this article, we examine various deep learning techniques on the SheEMO dataset.
  arXiv  Detail & Related papers  (2022-04-28T16:02:05Z)
• Deep Reinforcement Learning in Computer Vision: A Comprehensive Survey [29.309914600633032]
  Deep reinforcement learning augments the reinforcement learning framework and utilizes the powerful representation of deep neural networks. Recent works have demonstrated the remarkable successes of deep reinforcement learning in various domains including finance, medicine, healthcare, video games, robotics, and computer vision.
  arXiv  Detail & Related papers  (2021-08-25T23:01:48Z)
• Ten Quick Tips for Deep Learning in Biology [116.78436313026478]
  Machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Deep learning has become its own subfield of machine learning. In the context of biological research, deep learning has been increasingly used to derive novel insights from high-dimensional biological data.
  arXiv  Detail & Related papers  (2021-05-29T21:02:44Z)
• A Review on Explainability in Multimodal Deep Neural Nets [2.3204178451683264]
  multimodal AI techniques have achieved much success in several application domains. Despite their outstanding performance, the complex, opaque and black-box nature of the deep neural nets limits their social acceptance and usability. This paper extensively reviews the present literature to present a comprehensive survey and commentary on the explainability in multimodal deep neural nets.
  arXiv  Detail & Related papers  (2021-05-17T14:17:49Z)
• Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units and a Unified Framework [83.21732533130846]
  The paper focuses on large in-the-wild databases, i.e., Aff-Wild and Aff-Wild2. It presents the design of two classes of deep neural networks trained with these databases. A novel multi-task and holistic framework is presented which is able to jointly learn and effectively generalize and perform affect recognition.
  arXiv  Detail & Related papers  (2021-03-29T17:36:20Z)
• Hierarchical Learning Using Deep Optimum-Path Forest [55.60116686945561]
  Bag-of-Visual Words (BoVW) and deep learning techniques have been widely used in several domains, which include computer-assisted medical diagnoses. In this work, we are interested in developing tools for the automatic identification of Parkinson's disease using machine learning and the concept of BoVW.
  arXiv  Detail & Related papers  (2021-02-18T13:02:40Z)
• Knowledge as Invariance -- History and Perspectives of Knowledge-augmented Machine Learning [69.99522650448213]
  Research in machine learning is at a turning point. Research interests are shifting away from increasing the performance of highly parameterized models to exceedingly specific tasks. This white paper provides an introduction and discussion of this emerging field in machine learning research.
  arXiv  Detail & Related papers  (2020-12-21T15:07:19Z)
• Sequential Interpretability: Methods, Applications, and Future Direction for Understanding Deep Learning Models in the Context of Sequential Data [1.8275108630751837]
  We review current techniques for interpreting deep learning techniques involving sequential data. We identify similarities to non-sequential methods, and discuss current limitations and future avenues of sequential interpretability research.
  arXiv  Detail & Related papers  (2020-04-27T00:58:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.