Hand-Written Text Recognition Methods: Review Study

Odeh, Ayman; Odeh, Munther; Odeh, Hussein; Odeh, Nada

doi:10.18280/ria.360219

Cited by 2 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the field of machine vision and pattern processing, manuscript recognition is one of the most active investigation areas [1][2][3]. The characteristics of handwriting depend on the writing styles of the language.…”

Section: Introductionmentioning

confidence: 99%

Offline Author Identification using Non-Congruent Handwriting Data Based on Deep Convolutional Neural Network

LIU,

Meng,

ZHANG

2024

IJACSA

View full text Add to dashboard Cite

This investigation presents a novel technique for offline author identification using handwriting samples across diverse experimental conditions, addressing the intricacies of various writing styles and the imperative for organizations to authenticate authorship. Notably, the study leverages inconsistent data and develops a method independent of language constraints. Utilizing a comprehensive dataset adhering to American Society for Testing and Materials (ASTM) standards, a deep convolutional neural network (DCNN) model, enhanced with pre-trained networks, extracts features hierarchically from raw manuscript data. The inclusion of heterogeneous data underscores a significant advantage of this study, while the applicability of the proposed DCNN model to multiple languages further highlights its versatility. Experimental results demonstrate the efficacy of the proposed method in author identification. Specifically, the proposed model outperforms conventional approaches across four comprehensive datasets, exhibiting superior accuracy. Comparative analysis with engineering features and traditional methods such as Support Vector Machine (SVM) and Backpropagation Neural Network (BPNN) underscores the superiority of the proposed technique, yielding approximately a 13% increase in identification accuracy while reducing reliance on expert knowledge. The validation results, showcase the diminishing network error and increasing accuracy, with the proposed model achieving 99% accuracy after 200 iterations, surpassing the performance of the LeNet model. These findings underscore the robustness and utility of the proposed technique in diverse applications, positioning it as a valuable asset for handwriting recognition experts.

show abstract

Section: Introductionmentioning

confidence: 99%

Offline Author Identification using Non-Congruent Handwriting Data Based on Deep Convolutional Neural Network

LIU,

Meng,

ZHANG

2024

IJACSA

View full text Add to dashboard Cite

show abstract

“…Traditional text recognition methods discern textual meaning within images by analyzing intrinsic characteristics [19], such as structure [20], patterns [21], and unique shapes [22,23]. These approaches lack universality, necessitate manual feature selection to extract textual regions, and require adjustments to multiple parameters for different images, exhibiting inferior robustness [24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

A Non-Intrusive Automated Testing System for Internet of Vehicles App Based on Deep Learning

Zhang

Sun

2023

Electronics

View full text Add to dashboard Cite

In the non-intrusive automated testing system for Internet of Vehicles (IoV) applications, automatic recognition of text and icons on vehicle central control screens is of paramount importance. However, the detection and recognition of content on vehicle central control screens are inherently complex. Additionally, during non-intrusive vehicle central control screen image testing, there is a deficiency of suitable datasets and detection methods. This deficiency renders information within vehicle application images difficult to be accurately extracted by the detection network. To address this problem, this study first constructs a dataset tailored for text detection and recognition on vehicle screens. This dataset encompasses a variety of vehicle central control images, enabling the generic text detection and recognition network to more effectively identify and interpret text within vehicle screens. Subsequently, this research proposes an enhanced Fully Convolutional Networks for Text Detection (FOTS) method for vehicle central control screen text detection and recognition. This method elevates the semantic expression capabilities of features by sharing vehicle central control screen text detection and recognition features. Furthermore, it improves multi-scale feature processing capabilities through the utilization of a feature transformation module. Validation through visual and quantitative experiments demonstrates that the proposed method can effectively accomplish text detection and recognition tasks on vehicle screens. This achievement bears significant implications for the field of automated testing in IoV applications.

show abstract

Hand-Written Text Recognition Methods: Review Study

Cited by 2 publications

References 0 publications

Offline Author Identification using Non-Congruent Handwriting Data Based on Deep Convolutional Neural Network

Offline Author Identification using Non-Congruent Handwriting Data Based on Deep Convolutional Neural Network

A Non-Intrusive Automated Testing System for Internet of Vehicles App Based on Deep Learning

Contact Info

Product

Resources

About