Advances in online handwritten recognition in the last decades

Ghosh, Trishita; Sen, Shibaprasad; Obaidullah, Sk Md; Santosh, K. C.; Pal, Umapada

doi:10.1016/j.cosrev.2022.100515

Cited by 21 publications

(9 citation statements)

References 133 publications

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“…Character segmentation is done using horizontal and vertical projection on handwritten text. In [11], researchers examine Academic literature proposes algorithms for online handwriting and machine-printed Assamese language text recognition. The Assamese language has more cursive writing than English and others.…”

Section: Related Studiesmentioning

confidence: 99%

Enhancing Assamese Word Recognition for CBIR: A Comparative Study of Ensemble Methods and Feature Extraction Techniques

Borah,

Baruah,

Dey

et al. 2023

IJACSA

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This study conducts a thorough assessment of ensemble machine learning methods, specifically focusing on the identification of Assamese words. This task is crucial for improving Content-Based Image Retrieval systems and safeguarding the digital heritage of Assamese culture. We analyze the efficacy of different algorithms, such as CatBoost, XGBoost, Gradient Boosting, Random Forest, Bagging, AdaBoost, Stacking, and Histogram-Based Gradient Boosting, by thoroughly examining their performance in terms of accuracy, precision, recall, Kappa, F1-score, Matthews Correlation Coefficient, and AUC. The Cat-Boost algorithm stands out as the top performer, achieving an accuracy rate of 97.7%, precision rate of 95%, and recall rate of 96%. XGBoost is also acknowledged for its substantial effectiveness. This comparative analysis emphasizes CatBoost's superiority in terms of precision and recall. Additionally, it underscores the strong ability of ensemble classifiers to enhance assistive technologies, promote social inclusivity, and seamlessly integrate the Assamese language into technological applications.

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Section: Related Studiesmentioning

confidence: 99%

Enhancing Assamese Word Recognition for CBIR: A Comparative Study of Ensemble Methods and Feature Extraction Techniques

Borah,

Baruah,

Dey

et al. 2023

IJACSA

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“…Handwritten documents can become damaged over time. Thus, appropriate indexing and storage systems are crucial [3]. Numerous exciting features of numerical reports include organizing and ordering a vast amount of data for further processing, such as record recovery.…”

Section: Introductionmentioning

confidence: 99%

“…Online and offline handwriting detection are the two groups used in the past. Due to the extensive use of touchless technology, handwriting is subclassified into offline, online and air-written [3]. When a printed or handwritten document is scanned to produce a number form, the writing on the offline paper can be recognized.…”

Section: Introductionmentioning

confidence: 99%

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A Comprehensive Literature Review on Air-written Online Handwritten Recognition

K. Jabde,

H. Patil,

D. Vibhute

et al. 2024

IJCDS

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The COVID-19 pandemic has shifted ordinary life into a digital platform. Individuals rapidly use digital or online media to avoid the impact of touch-based platforms. In this case, touchless technology is primarily used in operation theatres, online education systems, ticket counters, etc. However, the online numerals or characters written in the air are challenging to recognize efficiently due to the writing styles of every individual. Therefore, the present research focuses on recent work on online air-written handwritten recognition. Almost eighty-plus standard articles on handwriting recognition from various journal/conference databases are considered for the current review. In addition, the generalized methodology of handwriting recognition is also provided for new research. It is observed that some online handwritten databases for some languages are freely available for research purposes. However, the online real-time air-written numeral dataset is private for Devanagari and English. Therefore, designing and developing a standard dataset for character or numeral recognition written in the air is suggested. The earlier studies achieved satisfactory results on air-writing multilingual numerals collected via or without sensors using advanced machine learning and deep learning methods. The convolutional neural network (CNN) has provided the best accuracy for several languages, excluding Devanagari.

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“…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

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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.

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Advances in online handwritten recognition in the last decades

Cited by 21 publications

References 133 publications

Enhancing Assamese Word Recognition for CBIR: A Comparative Study of Ensemble Methods and Feature Extraction Techniques

Enhancing Assamese Word Recognition for CBIR: A Comparative Study of Ensemble Methods and Feature Extraction Techniques

A Comprehensive Literature Review on Air-written Online Handwritten Recognition

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

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