H‐WordNet: a holistic convolutional neural network approach for handwritten word recognition

Das, Dibyasundar; Nayak, Deepak Ranjan; Dash, Ratnakar; Majhi, Banshidhar; Zhang, Yudong

doi:10.1049/iet-ipr.2019.1398

Cited by 22 publications

(19 citation statements)

References 54 publications

(84 reference statements)

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“…Dealing with images directly reduces the performance of existing neural networks. Thus, a handcraft feature extraction step Resized image by [32] is utilized in most conventional approaches [1]. The endto-end learning in the absence of handcrafted features is one of the remarkable characteristics of the CNN.…”

Section: Proposed Cnn Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Handwriting recognition refers to the process of converting handwritten images into their corresponding editable files [1,2]. Unlike printed texts, due to significant changes in writing style and shape, the skew or slant automatic handwritten text recognition is still a debatable subject in the pattern recognition and computer vision community [1]. Handwriting recognition has several applications such as bank cheque processing [3], the recognition of notes [4,5], postal address recognition [6] and historical documents [7,8] in various scripts including Indian, Chinese, Latin, Arabic, and Persian.…”

Section: Introductionmentioning

confidence: 99%

“…Handwritten recognition methods are divided into two major groups: on-line and off-line methods [10]. While on-line recognition depends on pen movement coordinates and the pen trajectory of the letter, off-line recognition is based on the analysis of the text image [1]. Also, there are two methods for word recognition: *Corresponding Author Institutional Email: a.zohrevand@kub.ac.ir (A.…”

Section: Introductionmentioning

confidence: 99%

“…• Inspiring by [1], this paper proposed an end-to-end learning architecture that eliminates the need for handcrafted feature extraction in holistic Persian off-line handwritten word recognition. • Presenting a new Transfer Learning (TL) approach for holistic Persian off-line handwritten word recognition.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Holistic Persian Handwritten Word Recognition Using Convolutional Neural Network

Zohrevand

Imani

2021

IJE

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Due to the cursive-ness and high variability of Persian script, the segmentation of handwritten words into sub-words is still a challenging task. These issues could be addressed in a holistic approach by sidestepping segmentation at the character level. In this paper, an end-to-end holistic method based on deep convolutional neural network is proposed to recognize off-line Persian handwritten words. The proposed model uses only five convolutional layers and two fully connected layers for classifying word images effectively, which can lead to a substantial reduction in the required parameters. The effect of various pooling strategies is also investigated in this paper. The primary goal of this article is to ignore handcrafted feature extraction and to attain a generalized and stable word recognition system. The presented model is assessed using two famous handwritten Persian word databases called Sadri and IRANSHAHR. The recognition accuracies were obtained at 98.6% and 94.6%, on Sadri and IRANSHAHR datasets respectively, and outperformed the state-of-the-art methods.

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Section: Proposed Cnn Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Holistic Persian Handwritten Word Recognition Using Convolutional Neural Network

Zohrevand

Imani

2021

IJE

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Comparison of Persian Handwritten Digit Recognition in Three Color Modalities Using Deep Neural Networks

Zohrevand

Sattari

Sadri

et al. 2020

Pattern Recognition and Artificial Intelligence

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Most of the methods on handwritten recognition in the literature are focused and evaluated on Black and White (BW) image databases. In this paper we try to answer a fundamental question in document recognition. Using Convolutional Neural Networks (CNNs), as eye simulator, we investigate to see whether color modalities of handwritten digits and words affect their recognition accuracy or speed? To the best of our knowledge, so far this question has not been answered due to the lack of handwritten databases that have all three color modalities of handwritings. To answer this question, we selected 13,330 isolated digits and 62,500 words from a novel Persian handwritten database, which have three different color modalities and are unique in term of size and variety. Our selected datasets are divided into training, validation, and testing sets. Afterwards, similar conventional CNN models are trained with the training samples. While the experimental results on the testing set show that CNN on the BW digit and word images has a higher performance compared to the other two color modalities, in general there are no significant differences for network accuracy in different color modalities. Also, comparisons of training times in three color modalities show that recognition of handwritten digits and words in BW images using CNN is much more efficient.

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Enhancement of Deep Learning in Image Classification Performance Using VGG16 with Swish Activation Function for Breast Cancer Detection

Muduli

Dash

Majhi

2021

Communications in Computer and Information Science

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H‐WordNet: a holistic convolutional neural network approach for handwritten word recognition

Cited by 22 publications

References 54 publications

Holistic Persian Handwritten Word Recognition Using Convolutional Neural Network

Holistic Persian Handwritten Word Recognition Using Convolutional Neural Network

Comparison of Persian Handwritten Digit Recognition in Three Color Modalities Using Deep Neural Networks

Enhancement of Deep Learning in Image Classification Performance Using VGG16 with Swish Activation Function for Breast Cancer Detection

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