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.
Abstract (translated)
深度学习表示了一种将原始输入合并到中间特征层中的机器学习算法。这些深度学习算法在各种领域都取得了巨大的成功。在计算机视觉和模式识别领域，深度学习取得了人类水平性能的显著进步。为了在多样领域实现最先进的性能，深度学习使用了不同的架构，这些架构使用激活函数在隐藏和输出层之间执行各种计算。本文对手写识别领域现有研究的调查结果进行了综述。尽管最近的研究表明，深度学习方法为加快或在手写识别中证明准确结果提供了有价值的方法，但根据广泛的文献调查，当前研究尚未推翻现状，解决这一领域内的许多最紧迫的问题，但在先前的技术水平上取得了重要进展。此外，训练数据不足的问题也存在于这个领域。然而，本文的手写识别调查展望了深度学习在 bench 和 bedside 的变革，具有将多个领域（如图像处理、语音识别、计算机视觉、机器翻译、机器人与控制、医学影像、医学信息处理、生物信息学、自然语言处理、网络安全等）进行变革的可能，这使得深度学习在各个领域都具有广泛的应用前景。
URL
https://arxiv.org/abs/2404.08011
