A Zone Based Approach for Classification and Recognition Of Telugu Handwritten Characters

Rani, N. Shobha; Verma, Sanjay Kumar; Joseph, Anitta

doi:10.11591/ijece.v6i4.10553

Cited by 4 publications

(5 citation statements)

References 4 publications

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“…The above plots show that inception V1 can attain accuracy rate nearer to VGG with short training period. Some researchers have delved into the development of Telugu OCR systems [14], [24], [25]. [24] Handwritten Telugu characters SVM classifier 80% Rao [25] Handwritten Telugu text Hidden Markov models (HMMs), Akshara models and Akshara Bigram language models 74% Mathew [14] Scene…”

Section: Resultsmentioning

confidence: 99%

Computationally efficient handwritten Telugu text recognition

Revathi,

Prasad,

Gattim

2024

IJEECS

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<p>Optical character recognition (OCR) for regional languages is difficult due to their complex orthographic structure, lack of dataset resources, a greater number of characters and similarity in structure between characters. Telugu is popular language in states of Andhra and Telangana. Telugu exhibits distinct separation between characters within a word, making a character-level dataset sufficient. With a smaller dataset, we can effectively recognize more words. However, challenges arise during the training of compound characters, which are combinations of vowels and consonants. These are considered as two or more characters based on associated vattus and dheerghams with the base character. To address this challenge, each compound character is encoded into a numerical value and used as input during training, with subsequent retrieval during recognition. The segmentation issue arises from overlapping characters caused by varying handwritten styles. For handling segmentation issues at the character level arising from handwritten styles, we have proposed an algorithm based on the language's features. To enhance word-level accuracy a dictionary-based model was devised. A neural network utilizing the inception module is employed for feature extraction at various scales, achieving word-level accuracy rates of 78% with fewer trainable parameters.</p>

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Section: Resultsmentioning

confidence: 99%

Computationally efficient handwritten Telugu text recognition

Revathi,

Prasad,

Gattim

2024

IJEECS

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“…One of the potential reasons of the lack in their usage is the huge amount of produce features. Recently, it was used in object recognition researches but with the same [11]. Since TGH assigns each image pixel to the corresponding feature.…”

Section: Topographical Features Tghmentioning

confidence: 99%

Face recognition using selected topographical features

Naji

Salman

Fadhil

2020

IJECE

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This paper represents a new features selection method to improve an existed feature type. Topographical (TGH) features provide large set of features by assigning each image pixel to the related feature depending on image gradient and Hessian matrix. Such type of features was handled by a proposed features selection method. A face recognition feature selector (FRFS) method is presented to inspect TGH features. FRFS depends in its main concept on linear discriminant analysis (LDA) technique, which is used in evaluating features efficiency. FRFS studies feature behavior over a dataset of images to determine the level of its performance. At the end, each feature is assigned to its related level of performance with different levels of performance over the whole image. Depending on a chosen threshold, the highest set of features is selected to be classified by SVM classifier

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“…The algorithm developed has achieved an accuracy of around 100% and works for both front and rear images of the car. Rani, N.,et al(2016) [16]In research work mainly focus on evaluating the performance of various feature extraction techniques with respect to Telugu character recognition systems and analyze its efficiencies and accuracies in recognition of Telugu character set".…”

Section: Literature Surveymentioning

confidence: 99%

“…Median filtering is employed to get rid of unwanted noise from the image whereas protective the originality of the image [15] [16]. The image is regenerate into grayscale image.To this extracted image median filter is applied so as to get rid of the noise within the image.…”

Section: Median Filteringmentioning

confidence: 99%

Color detection in RGB-modeled images using MAT LAB

Duth¹,

M²

2018

IJET

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This research work introduces a method of using color thresholds to identify two-dimensional images in MATLAB using the RGB Color model to recognize the Color preferred by the user in the picture. Methodologies including image color detection convert a 3-D RGB Image into a Gray-scale Image, at that point subtract the two pictures to obtain a 2-D black-and-white picture, filtering the noise picture elements using a median filter, detecting with a connected component mark digital pictures in the connected area and utilize the bounding box and its properties to calculate the metric for every marking area. In addition, the shade of the picture element is identified by examining the RGB value of every picture element present in the picture. Color Detection algorithm is executed utilizing the MATLAB Picture handling Toolkit. The result of this implementation can be used in as a bit of security applications such as spy robots, object tracking, Color-based object isolation, and intrusion detection.

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A Zone Based Approach for Classification and Recognition Of Telugu Handwritten Characters

Cited by 4 publications

References 4 publications

Computationally efficient handwritten Telugu text recognition

Computationally efficient handwritten Telugu text recognition

Face recognition using selected topographical features

Color detection in RGB-modeled images using MAT LAB

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