Improving Recognition of Handwritten Kannada Characters Using Mixup Regularization

Hebbi, Chandravva; Maiya, Anirudh; Mamatha, H. R.

doi:10.1007/978-3-030-95502-1_33

Cited by 1 publication

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hebbi and Mamatha [20] generated a handwritten Kannada text dataset at the character level considering the K-means clustering algorithm in their process and utilizing different language aspects such as vowels and modifiers, etc. SVM with HOG features, CNN classifiers, and the ResNet18 model were used to investigate approach accuracy considering three levels of zones.…”

Section: Cnn and Svmmentioning

confidence: 99%

“…Therefore, the scientists have explored various machine learning methods, such as support vector machines (SVM) [15], random forests (RF) [16], k nearest neighbors (kNN) [17], decision trees (DT) [18], neural networks [19], and others. They have integrated these machine learning techniques with image processing methods to enhance the accuracy of OCR systems [20]. Lately, the research focus has shifted towards developing approaches for digitizing handwritten documents, primarily utilizing deep learning.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Handwritten Recognition Techniques: A Comprehensive Review

Alhamad,

Shehab,

Shambour

et al. 2024

Symmetry

View full text Add to dashboard Cite

Given the prevalence of handwritten documents in human interactions, optical character recognition (OCR) for documents holds immense practical value. OCR is a field that empowers the translation of various document types and images into data that can be analyzed, edited, and searched. In handwritten recognition techniques, symmetry can be crucial to improving accuracy. It can be used as a preprocessing step to normalize the input data, making it easier for the recognition algorithm to identify and classify characters accurately. This review paper aims to summarize the research conducted on character recognition for handwritten documents and offer insights into future research directions. Within this review, the research articles focused on handwritten OCR were gathered, synthesized, and examined, along with closely related topics, published between 2019 and the first quarter of 2024. Well-established electronic databases and a predefined review protocol were utilized for article selection. The articles were identified through keyword, forward, and backward reference searches to comprehensively cover all relevant literature. Following a rigorous selection process, 116 articles were included in this systematic literature review. This review article presents cutting-edge achievements and techniques in OCR and underscores areas where further research is needed.

show abstract

Section: Cnn and Svmmentioning

confidence: 99%