Script identification algorithms: a survey

Sahare, Parul; Dhok, Sanjay B.

doi:10.1007/s13735-017-0130-2

Cited by 23 publications

(3 citation statements)

References 98 publications

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“…For instance, the Brahmic script uses the Abugida writing system, which is used for most Indic languages [4]. Numerous character recognition algorithms have been proposed to transition to a paperless society [5], [6], [7], [8]. The character recognition survey studies come in several forms, but most are based on offline character recognition.…”

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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Section: Introductionmentioning

confidence: 99%

“…Script recognition is essential for automatically understanding papers that are written in hand. Authors categorized and examined numerous script identification techniques for document photographs in [7], and they compared the various schemes based on their advantages and shortcomings.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Literature Review on Air-written Online Handwritten Recognition

K. Jabde,

H. Patil,

D. Vibhute

et al. 2024

IJCDS

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“…Script type identification plays a significant role of identifying the script type of the text, from mono/multi-lingual text document, and feeding the text to appropriate OCR for character recognition. Many methods have been proposed in the literature for script type identification from a multiscript/monoscript document images [1]. Significant work has been carried out for printed documents compared to handwritten documents [2].…”

Section: Introductionmentioning

confidence: 99%

Multi-Feature based Handwritten Script Identification at word level

Ummapure*,

Rajput

2019

IJITEE

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SIFT and LBP are two popular techniques used for obtaining “feature description" of the object. SIFT identifies key points that are locations with distinct image information and robust to scaling and rotation whereas, LBP transforms an image into an array of integer labels describing small scale appearance of the image. In this paper, we present an efficient method wherein “feature description” of handwritten document images at word level are computed using SIFT and LBP. Identification of script type is done using KNN and SVM classifiers. Experimental results show that the performance of SVM is better over KNN. Further, the proposed method is compared with other methods in the literature to demonstrate the efficacy of the proposed method.

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Language-Based Classification of Document Images Using Hybrid Texture Features

Dixit

Shirdhonkar

2019

Advances in Biometrics

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Script identification algorithms: a survey

Cited by 23 publications

References 98 publications

A Comprehensive Literature Review on Air-written Online Handwritten Recognition

A Comprehensive Literature Review on Air-written Online Handwritten Recognition

Multi-Feature based Handwritten Script Identification at word level

Language-Based Classification of Document Images Using Hybrid Texture Features

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