A survey of offline handwritten Hindi character recognition

Indian, Ajay; Bhatia, Karamjit

doi:10.1109/icaccaf.2017.8344697

Cited by 7 publications

(2 citation statements)

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“…[32]. The offline handwriting character can be found using any of the following processes: picture capture, preprocessing, segmentation, feature extraction, selection, and categorization [33]. For OCR, it is necessary to have a preprocessed scanned document and a good resolution, but owing to scanning, this type of image has poor resolution [34].…”

Section: A Offline Handwriting Recognitionmentioning

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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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: A Offline Handwriting Recognitionmentioning

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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“…Hindi is another Indo Aryan language [8 ]. It is not yet legalized as a national language, yet preferred to be one because it is spoken by 425 million people in India as the first language and by more than 120 million people as the second language.…”

Section: Introductionmentioning

confidence: 99%

Telugu and Hindi Script Recognition using Deep learning Techniques

Sujatha¹,

Bhaskari²

2019

IJITEE

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The need for offline handwritten character recognition is intense, yet difficult as the writing varies from person to person and also depends on various other factors connected to the attitude and mood of the person. However, we are able to achieve it by converting the handwritten document into digital form. It has been advanced with introducing convolutional neural networks and is further productive with pre-trained models which have the capacity of decreasing the training time and increasing accuracy of character recognition. Research in recognition of handwritten characters for Indian languages is less when compared to other languages like English, Latin, Chinese etc., mainly because it is a multilingual country. Recognition of Telugu and Hindi characters are more difficult as the script of these languages is mostly cursive and are with more diacritics. So the research work in this line is to have inclination towards accuracy in their recognition. Some research has already been started and is successful up to eighty percent in offline hand written character recognition of Telugu and Hindi. The proposed work focuses on increasing accuracy in less time in recognition of these selected languages and is able to reach the expectant values.

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