Segmentation of Isolated and Touching Characters in Offline Handwritten Gurmukhi Script Recognition

Kumar, Munish; Jindal, M. K.; Sharma, Richa

doi:10.5815/ijitcs.2014.02.08

Cited by 32 publications

(8 citation statements)

References 9 publications

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“…The main aim of this experiment is to train the network to segment and identify the handwritten script. To perform the work first of all, we have done segmentation of character [15] [16], for which we have followed following steps fig.3 [6].…”

Section: Methodsmentioning

confidence: 99%

“…Image is scanned, pre-processing of the image is performed to eliminate inconsistency by reshaping and resizing the image. By applying Vertical Projection Line segmentation is performed after that Horizontal Projection is applied for Word segmentation, then by applying headline removal algorithm character is segmented [15][17] [18]. This generated Output is in the form of segmented characters, pre-processing of these characters are performed again and are feed to the CNN for real time prediction of trained model.…”

Section: Methodsmentioning

confidence: 99%

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Segmentation and Identification of Bilingual Offline Handwritten Scripts (Devanagari and Roman)

2019

IJRTE

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 Abstract: Hand written text acknowledgment field has been considered as one of the hardest issues in the digital word. The multifaceted nature dimension of this exploration zone is high due to the reasons like diverse method for writing pursued by the clients, auxiliary independences, age elements of people and so on. This paper shows a novel procedure for the recognition of handwritten scripts, for example division of words and characters. In this paper, we have used two different scripts :"Devanagari" and "Roman" scripts. For which three Convolution Neural Networks(CNN) models are applied on different types of classification: one for language classification for which we have achieved 98% accuracy, second one for Devanagari character classification for which we have achieved 89% and third one for Roman character classification for which have achieved 97% respectively.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Segmentation and Identification of Bilingual Offline Handwritten Scripts (Devanagari and Roman)

2019

IJRTE

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“…If poured water from top and bottom of the character, the cavity regions of the characters are known as reservoirs. This method cannot use for the broken or overlapping characters [4]. End Detection Algorithm: End detection algorithm is used to find that whether there is any touched character or not.…”

Section: Water Reserviour Methodmentioning

confidence: 99%

A Review on Segmentation of Touching and Broken Characters for Handwritten Gurmukhi Script

Kaur¹,

Bathla²

2015

IJCA

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Character Segmentation of Handwritten Documents has been an interested area of research and its applicable environment becomes it a challenging research topic. The desire to edit the scanned document leads to develop the idea of optical character recognition. Segmentation plays very important role in optical character recognition system. The incorrect segmentation is just like a garbage in and garbage out. Segmentation of the broken character is quite difficult because vertical profile projection technique assumes the broken parts of the characters as individual characters. Existing methods focuses only upon the single touching characters. But our main focus is to design a robust method for the segmentation of broken and multiple touching characters. Existing systems focus only on the segmentation of fixed sized characters. But we develop the size independent algorithm which works on variable size characters. Thus, in the proposed method we develop the algorithm which works on the segmentation of broken, multiple touching characters of independent size including the three zones of the handwritten gurmukhi script. The main challenges like as variation in handwriting style etc. make the segmentation to difficult. General TermsOptical Character Recognition

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“…Internal segmentation was used to extract isolates letters from cursive script. Internal segmentation was divided in Explicit Segmentation, Implicit Segmentation and Hybrid Segmentation [26,27,28]. Explicit Segmentation was used to find interconnections and cut the image according to the interconnections.…”

Section: Internal Segmentationmentioning

confidence: 99%

Review on Recent Advances in Automatic Handwritten MODI Script Recognition

Sadanand¹,

Borde²,

Ramesh³

et al. 2015

IJCA

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Many HOCR systems was developed for different foreign language and efforts on Indian script still verdant. Due to the varying writing style of each individual HOCR becomes a critical challenge since last few decades. India was known to world for its unity in diversity. The geographical and cultural vastness of the country various spoken languages and writing scripts were invented and utilized for daily communication. Courageous endeavors was made towards HOCR for various Indian scripts. The development of HOCR system was considered an active area of research due to the complexity and structure of Indian scripts. MODI Script was an ancient script of India as compared with other Indian scripts. Large number of historical documents exists and they need to be preserved and explored therefore special attention must be given to MODI script. This research paper is an attempt towards an investigation of recent advances in Handwritten Optical Character Recognition system for MODI script.

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Segmentation of Isolated and Touching Characters in Offline Handwritten Gurmukhi Script Recognition

Cited by 32 publications

References 9 publications

Segmentation and Identification of Bilingual Offline Handwritten Scripts (Devanagari and Roman)

Segmentation and Identification of Bilingual Offline Handwritten Scripts (Devanagari and Roman)

A Review on Segmentation of Touching and Broken Characters for Handwritten Gurmukhi Script

Review on Recent Advances in Automatic Handwritten MODI Script Recognition

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