Printed Gujarati Script OCR using Hopfield Neural Network

Solanki, Prachi; Bhatt, Malay

doi:10.5120/11905-7982

Cited by 8 publications

(5 citation statements)

References 11 publications

(8 reference statements)

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“…The study obtained 85.60% and 95.92% accuracy for Naive Bayes and ANN, respectively, indicating that ANN is better than KNN 4 in recognising the Gujarati numerals [46]. Apart from the recognition of numbers and characters in the Gujarati script, Solanki and Bhatt [47] applied PCA and ANN together to achieve a 93.25% accuracy in recognising Gujarati words.…”

Section: Literature Reviewmentioning

confidence: 99%

Brahmi Word Recognition by Supervised Techniques

Gautam¹,

Chai²,

Afrin³

et al. 2020

Preprint

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Significant progress has made in pattern recognition technology. However, one obstacle that has not yet overcome is the recognition of words in the Brahmi script, specifically the recognition of characters, compound characters, and word because of complex structure. For this kind of complex pattern recognition problem, it is always difficult to decide which feature extraction and classifier would be the best choice. Moreover, it is also true that different feature extraction and classifiers offer complementary information about the patterns to be classified. Therefore, combining feature extraction and classifiers, in an intelligent way, can be beneficial compared to using any single feature extraction. This study proposed the combination of HOG +zonal density with SVM to recognize the Brahmi words. Keeping these facts in mind, in this paper, information provided by structural and statistical based features are combined using SVM classifier for script recognition (word-level) purpose from the Brahmi words images. Brahmi word dataset contains 6,475 and 536 images of Brahmi words of 170 classes for the training and testing, respectively, and the database is made freely available. The word samples from the mentioned database are classified based on the confidence scores provided by support vector machine (SVM) classifier while HOG and zonal density use to extract the features of Brahmi words. Maximum accuracy suggested by system is 95.17% which is better than previously suggested studies.

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Section: Literature Reviewmentioning

confidence: 99%

Brahmi Word Recognition by Supervised Techniques

Gautam¹,

Chai²,

Afrin³

et al. 2020

Preprint

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“…Recognition of handwritten Chinese character using Hopfield neural networks of stroke extraction and pre-processing feature set of stroke and row-column assignments character matching [14]. The author proposed a model to recognize the printed Gujrati script using Hopfield neural network of Otsu's histogram by zone separation (upper, middle, lower zone) recognition of accuracy is 93.25% [15]. A model was proposed by the author to recognize the character of Hindi using neural networks of extraction of different styles and font's accuracy approximately 95.97% was achieved and recognition accuracy was 98% [16].…”

Section: Odia Numeralsmentioning

confidence: 99%

Odia Characters and Numerals Recognition using Hopfield Neural Network Based on Zoning Feature

Jena¹,

Pradhan²,

Biswal³

et al. 2019

IJRTE

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Odia character and digits recognition area are vital issues of these days in computer vision. In this paper a Hopfield neural network designe to solve the printed Odia character recognition has been discussed. Optical Character Recognition (OCR) is the principle of applying conversion of the pictures from handwritten, printed or typewritten to machine encoded text version. Artificial Neural Networks (ANNs) trained as a classifier and it had been trained, supported the rule of Hopfield Network by exploitation code designed within the MATLAB. Preprocessing of data (image acquisition, binarization, skeletonization, skew detection and correction, image cropping, resizing, implementation and digitalization) all these activities have been carried out using MATLAB. The OCR, designed a number of the thought accuses non-standard speech for different types of languages. Segmentation, feature extraction, classification tasks is the well-known techniques for reviewing of Odia characters and outlined with their weaknesses, relative strengths. It is expected that who are interested to figure within the field of recognition of Odia characters are described in this paper. Recognition of Odia printed characters, numerals, machine characters of research areas finds costly applications within the banks, industries, offices. In this proposed work we devolve an efficient and robust mechanism in which Odia characters are recognized by the Hopfield Neural Networks (HNN).

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“…Closing algorithm is applied to reduce the gap and link each connected component together. (2) Large character splitting using heuristic information, with the intention of character categorized by their size (width) in which if character size is less than heuristic size then it is categorized as a broken character. (3) Small characters merging using heuristic information.…”

Section: Fig 5 Connected Component Processing For Thai Characters [8]mentioning

confidence: 99%

“…The character segmentation is done using different type of segmentation methods. Then feature extraction is carried out based on the segmentation [2]. The features of input characters are compared with the database which has stored characters.…”

Section: Introductionmentioning

confidence: 99%

Challenges of broken characters in character segmentation method for Gujarati printed documents

Shah

Ratanpara

2015

2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS)

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Character segmentation is an important step of optical character recognition (OCR) system. The segmentation of broken characters is one of the key factors which affect the performance of recognition system. So it is necessary to make OCR systems more effective and accurate for broken character segmentation. This paper explains the challenges in broken character segmentation method for Guajarati printed documents. Features and limitations of various methods in broken character segmentation are discussed. Different types of broken characters in Guajarati language are explained. Several challenges are explained for Gujarati broken character segmentation which is useful to implement broken character segmentation method.

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Printed Gujarati Script OCR using Hopfield Neural Network

Cited by 8 publications

References 11 publications

Brahmi Word Recognition by Supervised Techniques

Brahmi Word Recognition by Supervised Techniques

Odia Characters and Numerals Recognition using Hopfield Neural Network Based on Zoning Feature

Challenges of broken characters in character segmentation method for Gujarati printed documents

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