Recognition of Multi-oriented Touching Characters in Graphical Documents

Roy, Partha Pratim; Pal, Umapada; Lladós, Josep

doi:10.1109/icvgip.2008.26

Cited by 12 publications

(2 citation statements)

References 20 publications

(18 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Roy et al [36] presented a work in which they separated a text and symbols from color graphics by using connected component features and geometrical features [37]. They also worked on Multioriented touching characters in the graphical documents.…”

Section: Applications Of Roman Character Recognition In License Platementioning

confidence: 99%

Recognition of Degraded and Non Degraded Roman Characters Using Different Classifiers

Verma¹,

Agarwal²,

Aggarwal³

2019

ujeee

View full text Add to dashboard Cite

In this paper, we present an efficient and robust technique for the recognition of offline roman characters. The main strategy is to extract statistical and similarity features using a combination of grey level co-occurrence matrix (GLCM) and complementary similarity measure (CSM) method. In this work, the CSM method is used to extract features from binary images and combined with GLCM to boost the accuracy of character recognition. The recognition has been done using four different classifiers i.e. artificial neural network (ANN), Naive Bayes classifier, random forest (RF) and support vector machine (SVM). The standard dataset has been used for experimental work. We have done experiments on the clean and noisy dataset. It achieves the accuracy of 100% for some characters without noise and 94.11% with impulsive noise. A comparison of these four classifiers is recorded with and without a noisy environment. On a clean dataset, the random forest provides the best average recognition accuracy of 84.9% for all characters. On low noise datasets, random forest and artificial neural networks have almost the same recognition accuracy and on high noise datasets, SVM provides the highest recognition accuracy.

show abstract

Section: Applications Of Roman Character Recognition In License Platementioning

confidence: 99%

Recognition of Degraded and Non Degraded Roman Characters Using Different Classifiers

Verma¹,

Agarwal²,

Aggarwal³

2019

ujeee

View full text Add to dashboard Cite

show abstract

“…Common techniques for character segmentation exploit several aspects characterizing letters and their shapes, such as vertical projection, pitch estimation or character size, contour analysis, or segmentation-recognition coupled techniques [24], [27]. One of the most difficult problem an image segmentation algorithm has to address is the segmentation of touching characters [38], [41]. Very often, adjacent characters are touching, and may overlap, making hard the task of segmenting a given expression or word correctly into its character components [4,22].…”

Section: Introductionmentioning

confidence: 99%

A fuzzy approach to segment touching characters

Farulla

Murru

Rossini

2017

Expert Systems with Applications

View full text Add to dashboard Cite

The problem of correctly segmenting touching characters is an hard task to solve and it is of major relevance in pattern recognition. In the recent years, many methods and algorithms have been proposed; still, a definitive solution is far from being found. In this paper, we propose a novel method based on fuzzy logic. The proposed method combines in a novel way three features for segmenting touching characters that have been already proposed in other studies but have been exploited only singularly so far. The proposed strategy is based on a 3-input/1-output fuzzy inference system with fuzzy rules specifically optimized for segmenting touching characters in the case of Latin printed and handwritten characters. The system performances are illustrated and supported by numerical examples showing that our approach can achieve a reasonable good overall accuracy in segmenting characters even on tricky conditions of touching characters. Moreover, numerical results suggest that the method can be applied to many different datasets of characters by means of a convenient tuning of the fuzzy sets and rules.

show abstract