Results 11 to 20 of about 41,791,707 (220)

HMM-based Offline Recognition of Handwritten Words Crossed Out with Different Kinds of Strokes [PDF]

, 2008
In this work, we investigate the recognition of words that have been crossed-out by the writers and are thus degraded. The degradation consists of one or more ink strokes that span the whole word length and simulate the signs that writers use to cross ...
Vinciarelli, A., Likforman-Sulem, L.
core   +8 more sources

Advancements and Challenges in Handwritten Text Recognition: A Comprehensive Survey [PDF]

Journal of Imaging
Franck Gechter, Laurent Heyberger, Christophe Guyeux   +2 more
exaly   +2 more sources

Handwritten text generation and strikethrough characters augmentation

Компьютерная оптика, 2022
We introduce two data augmentation techniques, which, used with a Resnet-BiLSTM-CTC network, significantly reduce Word Error Rate and Character Error Rate beyond best-reported results on handwriting text recognition tasks.
A.V. Shonenkov, D.K. Karachev, M.Y. Novopoltsev, M.S. Potanin, D.V. Dimitrov, A.V. Chertok   +5 more
doaj   +1 more source

Component-based Segmentation of words from handwritten Arabic text [PDF]

, 2009
Efficient preprocessing is very essential for automatic recognition of handwritten documents. In this paper, techniques on segmenting words in handwritten Arabic text are presented.
AlKhateeb, J. H., Jiang, J., Ipson, S., Ren, Jinchang   +3 more
core   +4 more sources

End-To-End Deep-Learning-Based Tamil Handwritten Document Recognition and Classification Model

IEEE Access, 2023
Overview: Handwriting recognition (HR) involves converting handwritten text into machine-readable text. Tamil handwritten document recognition remains a challenging process in various text real-world applications owing to the differences in the sizes ...
C. Vinotheni, S. Lakshmana Pandian
doaj   +1 more source

Watch Your Strokes: Improving Handwritten Text Recognition with Deformable Convolutions [PDF]

, 2021
Handwritten Text Recognition (HTR) in free-layout pages is a valuable yet challenging task which aims to automatically understand handwritten texts. State-of-the-art approaches in this field usually encode input images with Convolutional Neural Networks,
Rita Cucchiara, Massimiliano Corsini, Iulian Cojocaru, Lorenzo Baraldi, Silvia Cascianelli, Baraldi L., Cojocaru I., Corsini M., Cucchiara R., Cascianelli S.   +9 more
core   +1 more source

PHTI: Pashto Handwritten Text Imagebase for Deep Learning Applications

IEEE Access, 2022
Document Image Analysis (DIA) is one of the research areas of Artificial Intelligence (AI) that converts document images into machine-readable codes. In DIA systems, Optical Character Recognition (OCR) plays a key role in digitizing document images.
Ibrar Hussain, Riaz Ahmad, Siraj Muhammad, Khalil Ullah, Habib Shah, Abdallah Namoun   +5 more
doaj   +1 more source

End-to-End Historical Handwritten Ethiopic Text Recognition Using Deep Learning

IEEE Access, 2023
Recognizing handwritten text is a challenging task, especially for scripts with numerous alphabets and symbols. The Ethiopic script has a vast character set and is used for historical documents in typewritten, handwritten, and hand-printed forms. However,
Ruchika Malhotra, Maru Tesfaye Addis
doaj   +1 more source

A Residual-Attention Offline Handwritten Chinese Text Recognition Based on Fully Convolutional Neural Networks

IEEE Access, 2021
Offline handwritten Chinese text recognition is one of the most challenging tasks in that it involves various writing styles, complex character-touching, and large number of character categories.
Yintong Wang, Yingjie Yang, Weiping Ding, Shuo Li   +3 more
doaj   +1 more source

Handwritten Character Recognition to Obtain Editable Text [PDF]

E3S Web of Conferences, 2023
Optical character recognition involves distinguishing, grouping, and, in specific cases, remedying optical images/designs in a computerized picture.
Pravalika Jella, Korla Sitara, Kavya Erra, Sravani   +3 more
doaj   +1 more source