Results 41 to 50 of about 79,075 (205)

A Survey of Handwritten Character Recognition with MNIST and EMNIST

Applied Sciences, 2019
This paper summarizes the top state-of-the-art contributions reported on the MNIST dataset for handwritten digit recognition. This dataset has been extensively used to validate novel techniques in computer vision, and in recent years, many authors have ...
A. Baldominos, Y. Sáez, P. Isasi
semanticscholar   +1 more source

OCR-Nets: Variants of Pre-trained CNN for Urdu Handwritten Character Recognition via Transfer Learning

, 2020
Deep Convolutional neural networks (CNN) have been among the utmost competitive neural network architectures and have set the state-of-the-art in various fields of computer vision. In this paper, we present OCR-Nets, variants of (AlexNet & GoogleNet) for
Mohammed Aarif K.O., Sivakumar Poruran
semanticscholar   +1 more source

A New Hybrid Convolutional Neural Network and eXtreme Gradient Boosting Classifier for Recognizing Handwritten Ethiopian Characters

IEEE Access, 2020
Handwritten character recognition has been profoundly studied for many years in the field of pattern recognition. Due to its vast practical applications and financial implications, the handwritten character recognition is still an important research area.
Halefom Tekle Weldegebriel, Han Liu, Anwar Ul Haq, Emmanuel Bugingo, Defu Zhang   +4 more
doaj   +1 more source

Automatic Receipt Recognition System Based on Artificial Intelligence Technology

Applied Sciences, 2022
In this study, an automatic receipt recognition system (ARRS) is developed. First, a receipt is scanned for conversion into a high-resolution image. Receipt characters are automatically placed into two categories according to the receipt characteristics:
Cheng-Jian Lin, Yu-Cheng Liu, Chin-Ling Lee   +2 more
doaj   +1 more source

Student handwritten mathematical formula recognition model combining EfficientNet and GNN

Systems and Soft Computing
Recognizing handwritten mathematical formulas in student test papers can greatly increase the scoring speed. However, due to the instability of handwritten mathematical formulas, such as irregular writing and character distortion, the recognition effect ...
Jimin Guo
doaj   +1 more source

uTHCD: A New Benchmarking for Tamil Handwritten OCR

IEEE Access, 2021
The robustness of a typical Handwritten character recognition system relies on the availability of comprehensive supervised data samples. There has been considerable work reported in the literature about creating the database for several Indic scripts ...
Noushath Shaffi, Faizal Hajamohideen
doaj   +1 more source

Nepali Handwritten Character Recognition System

, 2022
{"references": ["Purohit, A., & Chauhan, S. S. (2016). A literature survey on handwritten character recognition. IJCSIT) International Journal of Computer Science and Information Technologies, 7(1), 1-5.", "Acharya, S., Pant, A. K., & Gyawali, P. K. (2015, December).
Acharya, Santosh, Shashank Dhungel, Jha, Ashish Kr.   +2 more
openaire   +1 more source

Bangla Handwritten Character Recognition using Convolutional Neural Network with Data Augmentation

International Conference on Informatics, Electronics and Vision, 2019
This paper proposes a process of Handwritten Character Recognition to recognize and convert images of individual Bangla handwritten characters into electronically editable format, which will create opportunities for further research and can also have ...
Rumman Rashid Chowdhury, Mohammad Shahadat Hossain, Raihan ul Islam, Karl Andersson, Sazzad Hossain   +4 more
semanticscholar   +1 more source

Embedded Large–Scale Handwritten Chinese Character Recognition [PDF]

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020
As handwriting input becomes more prevalent, the large symbol inventory required to support Chinese handwriting recognition poses unique challenges. This paper describes how the Apple deep learning recognition system can accurately handle up to 30,000 Chinese characters while running in real-time across a range of mobile devices.
Chherawala, Youssouf, Dolfing, Hans J. G. A., Dixon, Ryan S., Bellegarda, Jerome R.   +3 more
openaire   +2 more sources

Multimodal Handwritten Exam Text Recognition Based on Deep Learning

Applied Sciences
To address the complex challenge of recognizing mixed handwritten text in practical scenarios such as examination papers and to overcome the limitations of existing methods that typically focus on a single category, this paper proposes MHTR, a Multimodal
Hua Shi, Zhenhui Zhu, Chenxue Zhang, Xiaozhou Feng, Yonghang Wang   +4 more
doaj   +1 more source