A Convolutional Recurrent Neural Network for the Handwritten Text Recognition of Historical Greek Manuscripts

2021 ◽  
pp. 249-262
Author(s):  
K. Markou ◽  
L. Tsochatzidis ◽  
K. Zagoris ◽  
A. Papazoglou ◽  
X. Karagiannis ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Text Recognition ◽  
Handwritten Text ◽  
Handwritten Text Recognition

scholarly journals AHWR-Net: offline handwritten amharic word recognition using convolutional recurrent neural network

2021 ◽  
Vol 3 (8) ◽  
Author(s):  
Fetulhak Abdurahman ◽  
Eyob Sisay ◽  
Kinde Anlay Fante
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Feature Extraction ◽  
Word Recognition ◽  
Recurrent Neural Network ◽  
Text Recognition ◽  
Input Word ◽  
Handwritten Text ◽  
Handwritten Text Recognition ◽  
Word Images

AbstractAmharic ("Image missing") is the official language of the Federal Government of Ethiopia, with more than 27 million speakers. It uses an Ethiopic script, which has 238 core and 27 labialized characters. It is a low-resourced language, and a few attempts have been made so far for its handwritten text recognition. However, Amharic handwritten text recognition is challenging due to the very high similarity between characters. This paper presents a convolutional recurrent neural networks based offline handwritten Amharic word recognition system. The proposed framework comprises convolutional neural networks (CNNs) for feature extraction from input word images, recurrent neural network (RNNs) for sequence encoding, and connectionist temporal classification as a loss function. We designed a custom CNN model and compared its performance with three different state-of-the-art CNN models, including DenseNet-121, ResNet-50 and VGG-19 after modifying their architectures to fit our problem domain, for robust feature extraction from handwritten Amharic word images. We have conducted detailed experiments with different CNN and RNN architectures, input word image sizes, and applied data augmentation techniques to enhance performance of the proposed models. We have prepared a handwritten Amharic word dataset, HARD-I, which is available publicly for researchers. From the experiments on various recognition models using our dataset, a WER of 5.24 % and CER of 1.15 % were achieved using our best-performing recognition model. The proposed models achieve a competitive performance compared to existing models for offline handwritten Amharic word recognition.

scholarly journals Offline Handwritten Text Recognition and Signature Verification

2021 ◽  
Author(s):  
Rehaan Sajjad Arai ◽  
Skanda Shanubog A ◽  
Rithik Jain ◽  
Pushkar Kumar ◽  
Krupashankari Sandyal
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Recurrent Neural Network ◽  
Text Recognition ◽  
Signature Verification ◽  
Novel Technique ◽  
Handwritten Text ◽  
Offline Signature Verification ◽  
Handwritten Text Recognition ◽  
Connectionist Temporal Classification

Offline Handwritten Text Recognition (HTR) is one of the most interesting challenges in today's date in the field of Image processing. This paper introduces a novel technique to recognize the handwritten text by using Convolutional Recurrent Neural Network along with Connectionist Temporal Classification. This model makes use of the IAM dataset. Offline Signature Verification (SV) is another challenging task in the field of biometrics. This paper demonstrates a novel technique to verify the signature as an original or forged one, and makes use of the Convolutional Siamese network.

scholarly journals Recurrent Neural Network Transducer for Japanese and Chinese Offline Handwritten Text Recognition

2021 ◽  
pp. 364-376
Author(s):  
Trung Tan Ngo ◽  
Hung Tuan Nguyen ◽  
Nam Tuan Ly ◽  
Masaki Nakagawa
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Text Recognition ◽  
Handwritten Text ◽  
Handwritten Text Recognition

scholarly journals Offline Handwritten Text Recognition and Signature Verification

2021 ◽  
Author(s):  
Rehaan Sajjad Arai ◽  
Skanda Shanubog A ◽  
Rithik Jain ◽  
Pushkar Kumar ◽  
Krupashankari Sandyal
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Recurrent Neural Network ◽  
Text Recognition ◽  
Signature Verification ◽  
Novel Technique ◽  
Handwritten Text ◽  
Offline Signature Verification ◽  
Handwritten Text Recognition ◽  
Connectionist Temporal Classification

Offline Handwritten Text Recognition (HTR) is one of the most interesting challenges in today's date in the field of Image processing. This paper introduces a novel technique to recognize the handwritten text by using Convolutional Recurrent Neural Network along with Connectionist Temporal Classification. This model makes use of the IAM dataset. Offline Signature Verification (SV) is another challenging task in the field of biometrics. This paper demonstrates a novel technique to verify the signature as an original or forged one, and makes use of the Convolutional Siamese network.

Handwritten Text Recognition and Conversion Using Convolutional Neural Network (CNN) Based Deep Learning Model

2021 ◽  
Author(s):  
Jebaveerasingh Jebadurai ◽  
Immanuel Johnraja Jebadurai ◽  
Getzi Jeba Leelipushpam Paulraj ◽  
Sushen Vallabh Vangeepuram
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Learning Model ◽  
Text Recognition ◽  
Handwritten Text ◽  
Handwritten Text Recognition ◽  
Deep Learning Model

scholarly journals Hiragana Handwriting Recognition Using Deep Neural Network Search

2020 ◽  
Vol 14 (01) ◽  
pp. 161
Author(s):  
Rosalina Rosalina ◽  
Johanes Parlindungan Hutagalung ◽  
Genta Sahuri
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Deep Neural Network ◽  
Handwriting Recognition ◽  
Text Recognition ◽  
Android Application ◽  
Network Search ◽  
Handwritten Text ◽  
Handwritten Text Recognition ◽  
Computer Storage

<span id="orcid-id" class="orcid-id-https">These days there is a huge demand in “storing the information available in paper documents into a computer storage disk”. Digitizing manual filled forms lead to handwriting recognition, a process of translating handwriting into machine editable text. The main objective of this research is to to create an Android application able to recognize and predict the output of handwritten characters by training a neural network model. This research will implement deep neural network in recognizing handwritten text recognition especially to recognize digits, Latin / Alphabet and Hiragana, capture an image or choose the image from gallery to scan the handwritten text from the image, use the live camera to detect the handwritten text real – time without capturing an image and could copy the results of the output from the off-line recognition and share it to other platforms such as notes, Email, and social media. </span>

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