Accuracy Enhancement of Devanagari Character Recognition by Gray level Normalization

Jangid, Mahesh; Srivastava, Sumit

doi:10.1145/2967878.2967907

Cited by 6 publications

(2 citation statements)

References 8 publications

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“…However, a competitive OCR system for the Indian script is not available (14). The major challenges in the recognition of Indic script are natural variability associated with handwriting (48) and characters of similar shape (49). In handwritten character recognition, the feature vector is very high (50).…”

Section: Mini Review On Challenges In Ocrmentioning

confidence: 99%

Machine Learning: A Review on Supervised Classification Algorithms and their Applications to Optical Character Recognition in Indic Scripts

Singh¹,

Gupta²,

Garg³

2022

ECS Trans.

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Machine learning is a common application of artificial intelligence, which gives machines the ability to learn from data automatically and improve with experience without being explicitly programmed. Supervised learning is one of two broad areas of machine learning that deal with the task of learning a function for a current model based on past data training on input-output pairs of examples. This function enables the model to predict future outcomes for new inputs. Regression and classification are two supervised machine learning problems. Classification is the most common task that intelligent systems perform most often. This review describes the classification algorithm’s working and its applications to optical character recognition in Indic script. Devanagari and Gurumukhi scripts are chosen for this attempt. The detailed study presented in this article will be helpful to researchers working in the field of optical character recognition to understand where to use which machine learning algorithm for best results.

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Section: Mini Review On Challenges In Ocrmentioning

confidence: 99%

Machine Learning: A Review on Supervised Classification Algorithms and their Applications to Optical Character Recognition in Indic Scripts

Singh¹,

Gupta²,

Garg³

2022

ECS Trans.

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“…The Accuracy enhancement of handwritten devanagari character recognition using background elimination and gray level normalization techniques was proposed by Mahesh Jangidetal. [9]. The Best choice to extract the features from handwritten Devanagari characters using GLAC (Gradient Local Auto-Correlation) feature extraction technique is used for the experiment.…”

Section: Literature Workmentioning

confidence: 99%

Unconstrained Character Classification For Off-Line Handwritten Devanagari Script Using Multi-Class Model For SVM

Kumar¹

2017

IJRASET

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Hindi script is being used in various languages, for instance Marathi, Rajasthan, Sanskrit, and Nepali and it is also the script of Devanagari. It is observed that errors in classification mainly due to complex structures, incorrect segmentation and high unevenness in writing styles, classification of characters from the unconstrained script has become a burning vicinity of interest for researchers. Computer-based pattern recognition is a process that involves preprocessing, feature extraction, feature selection, and classification. In that article, we have extracted features from HOG, the novelty of this approach to attain better accuracy and reduce misclassification as well as for classification of handwritten characters with a multiclass model for SVM. Implementation has been performed using a self-created dataset of 40 users for handwritten Hindi characters. The experimental results obtained from this self-created dataset described the effectiveness of this system. The proposed system has faster speed and higher accuracy than the traditional Hindi OCR's. Enormous applications and the future necessities of optical character recognition area open new paths for researchers. An effort is made to address the most crucial consequences and it is also tried to foreground the better directions of the research till date. Our experimental results present the high performance of these features when classified using SVM classification.

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Challenges in Recognition of Online and Off-line Compound Handwritten Characters: A Review

Khobragade

Koli

Lanjewar

2019

Smart Innovation, Systems and Technologies

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Accuracy Enhancement of Devanagari Character Recognition by Gray level Normalization

Cited by 6 publications

References 8 publications

Machine Learning: A Review on Supervised Classification Algorithms and their Applications to Optical Character Recognition in Indic Scripts

Machine Learning: A Review on Supervised Classification Algorithms and their Applications to Optical Character Recognition in Indic Scripts

Unconstrained Character Classification For Off-Line Handwritten Devanagari Script Using Multi-Class Model For SVM

Challenges in Recognition of Online and Off-line Compound Handwritten Characters: A Review

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