An efficient segmentation technique for Devanagari offline handwritten scripts using the Feedforward Neural Network

Dhaka, Vijay Pal; Sharma, Manoj Kumar

doi:10.1007/s00521-015-1844-9

Cited by 23 publications

(10 citation statements)

References 13 publications

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“…The consequences of the algorithms are in the arrangement of lines/words/characters (see Figs. 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18). The PPTRPRT technique starts with Preprocessing algorithm by extracting text regions and segmenting the text lines from bilingual offline handwritten script (see Figs.…”

Section: Experiments (S) and Results (S)mentioning

confidence: 99%

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Pixel plot and trace based segmentation method for bilingual handwritten scripts using feedforward neural network

Sharma

Dhaka

2015

Neural Comput & Applic

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In the Indian subcontinent, a number of languages are in use, and an automatic recognition of printed and handwritten scripts facilitates number of applications such as image document sorting and penetrating online libraries of image documents. This framework proposed a bilingual (English and Hindi) character-spotting framework based on feedforward neural network which works on corpus of bilingual handwritten offline documents. The proposed Pixel Plot and Trace and Re-plot and Re-trace (PPTRPRT) framework traced the actual text region of the offline handwritten bilingual scripts and lead the process of line segmentation along with skew and de-skew operations. The findings of the iterations were adopted in pixel-spacebased word segmentation, which were further used in character segmentation. Moreover, PPTRPRT performs normalization operation to incorporate all pen-breadth deviations and inscription slant. The proposed framework was state of the art, reflected clearly from the findings of the framework. The proposed framework is proficient to character segmentation and provides accuracy up to 99.78 %.

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Section: Experiments (S) and Results (S)mentioning

confidence: 99%

“…Ryu et al [17] adopted structured SVM for the segmentation of language-free scripts. Manoj et al [18][19][20][21][22] employed FFNN for the segmentation of Devanagari handwritten scripts.…”

Section: Related Workmentioning

confidence: 99%

Pixel plot and trace based segmentation method for bilingual handwritten scripts using feedforward neural network

Sharma

Dhaka

2015

Neural Comput & Applic

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“…A possible location of the segmenting neighboring was described by a graph model. Ram et al [2,14,15] used text line contour estimation to analyze and place neighboring text line segments in their real boundaries. Impedovo et al [3] has proposed a database for handwritten cursive basic words recognition.…”

Section: Related Workmentioning

confidence: 99%

“…The high gratitude tariff has been published to recognize remote digits, characters or words [11,12,[15][16][17][18][19][20][21][22][23][24]. The gratitude performance achieved for recognition systems of unconstrained English offline handwritten cursive scripts is drastically poorer.…”

Section: Introductionmentioning

confidence: 98%

“…This technique will provide a concrete basis for design of optical character readers with the fine accuracy and low cost. In the realm of research, the ''Pixel Plot and Trace and Re-plot and Retrace'' (PPTRPRT) technique has been applied by the authors with Devanagari script segmentation [15] and has been newly applied in the reconstruction of offline handwritten English cursive scripts.…”

Section: Introductionmentioning

confidence: 99%

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Segmentation of english Offline handwritten cursive scripts using a feedforward neural network

Sharma

Dhaka

2015

Neural Comput & Applic

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In the present paper, we used the Pixel Plot and Trace and Re-plot and Re-trace (PPTRPRT) technique for English offline handwritten curve scripts and leads. Unlike other approaches, the PPTRPRT technique prioritizes segmentation of words and characters. The PPTRPRT technique extracts text regions from English offline handwritten cursive scripts and leads an iterative procedure for segmentation of text lines along with skew and de-skew operations. Iteration outcomes provide for pixel spacebased word segmentation which enables segmentation of characters. The PPTRPRT technique embraces various dispensations in segmentation of characters from English offline handwritten cursive scripts. Moreover, various normalization steps allow for deviations in pen breadth and inscription slant. Investigational outcomes show that the proposed technique is competent at extracting characters from English offline handwritten cursive scripts.

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RETRACTED CHAPTER: Remote Sensing Classification Under Deep Learning: A Review

Sharma

Verma

2019

Smart Systems and IoT: Innovations in Computing

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An efficient segmentation technique for Devanagari offline handwritten scripts using the Feedforward Neural Network

Cited by 23 publications

References 13 publications

Pixel plot and trace based segmentation method for bilingual handwritten scripts using feedforward neural network

Pixel plot and trace based segmentation method for bilingual handwritten scripts using feedforward neural network

Segmentation of english Offline handwritten cursive scripts using a feedforward neural network

RETRACTED CHAPTER: Remote Sensing Classification Under Deep Learning: A Review

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