Handwritten text segmentation using average longest path algorithm

Salvi, Dhaval; Zhou, Jun; Waggoner, Jarrell; Wang, Song

doi:10.1109/wacv.2013.6475061

Cited by 19 publications

(12 citation statements)

References 23 publications

(21 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The outcomes of the algorithms are in the form of lines, words and characters (see Figs. 1,2,3,4,5,6,7,8,9,10,11,12,13). The PPTRPRT technique starts its execution with the preprocessing algorithm by extracting text regions and segmenting text lines from English offline handwritten cursive script images (see Fig.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Dhaval et al [1] tested segmentation by evaluating the local stroke geometry (imposed the width, height and aspect-ratio constraints in resultant characters), without making limiting assumptions on the characters size and number of characters in a word. This approach found the segmented text with the most average liveliness of the resulting characters.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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

Sharma

Dhaka

2015

Neural Comput & Applic

View full text Add to dashboard Cite

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.

show abstract

Section: Experiments and Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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

Sharma

Dhaka

2015

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…This problem is difficult and may be resolved by jointly segmenting and recognizing the characters [27], [37], such a problem can be formulated as a multi-class classification problem with a large range of small snippets extracted from the different prototypes of characters [25].…”

Section: Manuscript Recognition Problemmentioning

confidence: 99%

Handwritten Character Recognition on Focused on the Segmentation of Character Prototypes in Small Strips

Benafia¹,

Mazouzi²,

Benafia³

2017

IJISA

View full text Add to dashboard Cite

Abstract-The automatic recognition of handwriting is a particularly complex operation. Until now, there is no algorithm able to recognize handwriting without that; there are assumptions to take in advance to facilitate the task of the process. A handwritten text can contain letters lowercase, uppercase letters, characters sticks and digits. Therefore, it is capital to know extract and separate all these different units in order to process them with specific algorithms to their class handwriting.In this paper, we present a system for unconstrained handwritten text recognition, which allows to achieve this operation thanks to an intelligent segmentation based on an iterative cutting in a multi-script environment.The results obtained from the experimental protocol reach an "equal error rate" (EER) neighboring to 6%. These calculations were calculated with a relatively small base; however this same rate can be decreased with great bases. Our results are extremely encouraging for the simple reason that our approach is situated in a more general context unlike other approaches which define several non-rigid assumptions; this clearly makes the problem simpler and may make it trivial.

show abstract

“…The adaptive RLSA is a substitution to the traditional RLSA in the meaning that auxiliary smoothing constraints are set expressing geometrical characteristics of vicinal bridged components. The satisfaction of these constraints is continued by substitution of background pixels with foreground pixels [43]. Saoi et al propose following stages: decomposing color image into RGB channel images and making 2D Wavelet Transform of each decomposed image, then using the unsupervised pixel block categorization with the k-means algorithm in incorporated feature vector space and integrating outputs of three channels by logical OR [44].…”

Section: Related Workmentioning

confidence: 99%

Scene Text Recognition using Artificial Neural Network: A Survey

Kumar¹,

Kumar²,

Mishra³

2016

IJCA

View full text Add to dashboard Cite

Nowadays, scene text recognition has become an important emerging area of research in the field of image processing. In image processing, character recognition boosts the complexity in the area of Artificial Intelligence. Character recognition is not easy for computer programs in comparison to humans. In the broad spectrum of things, it may consider that recognizing patterns is the only thing which humans can do well and computers cannot. There are many reasons including various sources of variability, hypothesis and absence of hard-and-fast rules that define the appearance of a visual character. Hence; there is an unavoidable requirement for heuristic deduction of rules from different samples. This review highlights the superiority of artificial neural networks, a popular area of Artificial Intelligence, over various other available methods like fuzzy logic and genetic algorithm. In this paper, two methods are listed for character recognition -offline and online. The -Offline‖ methods include Feature Extraction, Clustering, and Pattern Matching. Artificial neural networks use the static image properties. The online methods are divided into two methods, k-NN classifier and direction based algorithm. Thus, the scale of techniques available for scene text recognition deserves an admiration. This review gives a detail survey of use of artificial neural network in scene text recognition.

show abstract

Handwritten text segmentation using average longest path algorithm

Abstract: Offline handwritten text recognition is a very challenging problem. Aside

Cited by 19 publications

References 23 publications

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

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

Handwritten Character Recognition on Focused on the Segmentation of Character Prototypes in Small Strips

Scene Text Recognition using Artificial Neural Network: A Survey

Contact Info

Product

Resources

About