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A Novel Transfer Learning Approach upon Hindi, Arabic, and Bangla Numerals using Convolutional Neural Networks [article]

Abdul Kawsar Tushar, Akm Ashiquzzaman, Afia Afrin, Md. Rashedul Islam
2017 arXiv   pre-print
The model utilizes convolutional neural networks with backpropagation for error reduction and dropout for data overfitting.  ...  The output performance of the proposed neural network is shown to have closely matched other state-of-the-art methods using only a fraction of time used by the state-of-the-arts.  ...  Abdul Kawsar Tushar and Akm Ashiquzzaman contributed equally to this work.  ... 
arXiv:1707.08385v1 fatcat:uvj5sex5hzcyhdwxra622cek4a

A Novel Deep Convolutional Neural Network Architecture Based on Transfer Learning for Handwritten Urdu Character Recognition

2020 Tehnički Vjesnik  
In this paper, we propose a novel deep convolutional neural network for handwritten Urdu character recognition by transfer learning three pre-trained CNN models.  ...  Deep convolutional neural networks (CNN) have made a huge impact on computer vision and set the state-of-the-art in providing extremely definite classification results.  ...  Acknowledgement We are very much thankful to Saad bin Ahmed, ELDA,Hossein Khosravi and SAAD for providing us the UNHD, EMILLE, CDB/Farsi and DBAHCL dataset respectively.  ... 
doi:10.17559/tv-20190319095323 fatcat:42pfouhqbrgwvgwxppkxzrpoly

HCR-Net: A deep learning based script independent handwritten character recognition network [article]

Vinod Kumar Chauhan, Sukhdeep Singh, Anuj Sharma
2022 arXiv   pre-print
HCR-Net is based on a novel transfer learning approach for HCR, where some of lower layers of a pre-trained network are utilized.  ...  In this paper, we have proposed a novel deep learning architecture which exploits transfer learning and image-augmentation for end-to-end learning for script independent handwritten character recognition  ...  The proposed model uses a novel transfer learning approach for HCR, where a few layers of a pre-trained VGG16 network are used to initialize some part of HCR-Net.  ... 
arXiv:2108.06663v2 fatcat:mxq6psh6gjf6xp74rr5ozegyki

Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR) [article]

Jamshed Memon, Maira Sami, Rizwan Ahmed Khan
2020 arXiv   pre-print
During last decade, researchers have used artificial intelligence / machine learning tools to automatically analyze handwritten and printed documents in order to convert them into electronic format.  ...  Optical character recognition is a science that enables to translate various types of documents or images into analyzable, editable and searchable data.  ...  CNN (Convolutional Neural Network) and a LSTM (Long short-term Memory), which is a recurrent neural network based architecture were used on Urdu language dataset.  ... 
arXiv:2001.00139v1 fatcat:p3rdutz35besxfxf7suozt7r2u

Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)

Jamshed Memon, Maira Sami, Rizwan Ahmed Khan, Mueen Uddin
2020 IEEE Access  
During last decade, researchers have used artificial intelligence / machine learning tools to automatically analyze handwritten and printed documents in order to convert them into electronic format.  ...  Optical character recognition is a science that enables to translate various types of documents or images into analyzable, editable and searchable data.  ...  A recent study by [69] used fully convolutional neural network(FCNN) on IAM and RIMES datasets.  ... 
doi:10.1109/access.2020.3012542 fatcat:f5bfni5kbfhf3i63lvv3t6pena

Intelligent Character Recognition System Using Convolutional Neural Network

S. Suriya, Dhivya S, Balaji M
2020 EAI Endorsed Transactions on Cloud Systems  
The proposed approach is capable of recognizing characters in a variety of challenging conditions using the Convolutional Neural Network, where traditional character recognition systems fail, notably in  ...  Convolutional Neural Network differs from other approaches by extracting the features automatically.  ...  profound learning approaches like Convolutional Neural Network (CNN) With Optimizer RMSprop (Root Mean Square Propagation), Deep Feed Forward Neural Networks(DFFNN).  ... 
doi:10.4108/eai.16-10-2020.166659 fatcat:rrv3tyk2ezegdhcwsvuvvkgbrq

Computational intelligence in processing of speech acoustics: a survey

Amitoj Singh, Navkiran Kaur, Vinay Kukreja, Virender Kadyan, Munish Kumar
2022 Complex & Intelligent Systems  
This paper presents a comprehensive survey on the speech recognition techniques for non-Indian and Indian languages, and compiled some of the computational models used for processing speech acoustics.  ...  However, a limited number of automatic speech recognition systems are available for commercial use.  ...  There are number of architectures that are useful in implementing the deep learning concept: Restricted Boltzmann Machine (RBM), Deep Belief Network (DBN), Auto Encoder (AE), and Convolutional Neural Network  ... 
doi:10.1007/s40747-022-00665-1 fatcat:6pu2xccbq5as7bn2y2tav2fdwa

Quantitative assessment of spatial sound distortion by the semi-ideal recording point of a hear-through device

Pablo Hoffmann, Flemming Christensen, Dorte Hammersho/i
2013 Journal of the Acoustical Society of America  
We developed a sensorial substitution system from vision to audition. An artificial neural network is used to identify the important parts in the image.  ...  Fish, swimming at 1-4 m/s, quickly learned to approach the feed when they heard a sound.  ...  ., 16th International Conference on Cognitive and Neural Systems (2012) ] to the processing of sampled digital data from acoustic arrays.  ... 
doi:10.1121/1.4805375 fatcat:u4bvxh6karet3avpjdhpfwqpf4

Text-detection and -recognition from natural images

Hanaa Mahmood
2020
Machine learning and deep learning were used to accomplish this task.In this research, we conducted an in-depth literature review on the current detection and recognition methods used by researchers to  ...  with various scales and of different sizes.In this research, we propose a methodology to handle the problem of text detection by using novel combination and selection features to deal with the classification  ...  This results from the joining together of the recurrent neural network (RNN) and the convolutional neural network (CNN).  ... 
doi:10.26174/thesis.lboro.11816487 fatcat:u5m5vrca6bbyzb5jlrlyqaqcoy

LANGUAGE IN INDIA Strength for Today and Bright Hope for Tomorrow

M Thirumalai, B Mallikarjun, Sam, B A Sharada, A R Fatihi, Lakhan, Marie Jennifer, S M Bayer, G Ravichandran, L Baskaran, Ramamoorthy
2011 unpublished
The Tamil ancient tradition followed its own course in most areas and developed its own descriptive technical terms and language-specific features of the  ...  Sanskrit grammatical traditions influenced the development of grammars in several languages such as Telugu, Kannada and Malayalam.  ...  It will definitely be a great hindrance to effective communication using English on the part of the learners.  ... 
fatcat:c5fxjqh3frhr3bbaa4wdcxmqee

Quantitative assessment of spatial sound distortion by the semi-ideal recording point of a hear-through device

Pablo Hoffmann, Flemming Christensen, Dorte Hammershoi
2013 unpublished
We developed a sensorial substitution system from vision to audition. An artificial neural network is used to identify the important parts in the image.  ...  Fish, swimming at 1-4 m/s, quickly learned to approach the feed when they heard a sound.  ...  ., 16th International Conference on Cognitive and Neural Systems (2012) ] to the processing of sampled digital data from acoustic arrays.  ... 
doi:10.1121/1.4799631 fatcat:ox2s7bsahzbhhcy663rwvw2nxy