Recognition of Kannada Characters in Scene Images using Neural Networks

Siddiqua, Shahzia; Naveena, C.; Manvi, Sunilkumar S.

doi:10.1109/iciip47207.2019.8985672

Cited by 7 publications

(5 citation statements)

References 17 publications

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“…The language of Sabaean appertain to the South Arabian subdivision of the Semitic group of the Afroasiatic language family [7]. It is illustrious via the various persons of the ancient South Arabian cluster with usage of h to spot the third personality, and as a prefix of causative; the else languages whole usage s1 in these cases; therefore, Sabaean is known as h-language & the else s-languages [16]- [18]. Sabaean was done in the alphabet of South Arabian, and such that Arabic and Hebrew marked merely consonants, the simply warning of vowels being among matres lectionis.…”

Section: Language Backgroundmentioning

confidence: 99%

“…An included OCR with speech result method for the sightless has been advertising for English language by Xerox-Kurzweil [16]. At current, additional complicated optical readers are existed for Arabic, Japanese, Chinese and Roman text [18]. These people who read can procedure documents that have been type typeset, written, or printed different kinds of printers.…”

Section: Historical View Of Ocrmentioning

confidence: 99%

“…Rectifying and classifying such mistaken characters in each OCR-processed document physically is a boring task because of the data sheer volume. Accordingly, a method is necessary to identify this kind of OCR faults and rectify them in order to enforce standards of purity and archived data quality [18].…”

Section: Character Level Segmentationmentioning

confidence: 99%

See 2 more Smart Citations

Recognition of Handwritten Sheba Character using Artificial Neural Network

2021

IJATCSE

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From the Last twenty years, the computer-based mechanism has an essential process in daily life and research-oriented applications, the whole world be attracted by computers and approximately all the main processing is being completed automatically. Recognition of handwriting now it is an eye-catching and tough study analysis in image processing and pattern identification field in the today’s world. To beat this problem Optical Character Recognition system (OCR) is practice and concentrated research has been carrying on OCR. Numerous OCR systems are existing in the market other than mainly of this system working for Japanese, English, Chinese, Roman letters. There is no adequate work on Arabic language particularly Sheba characters. In proposed paper, an OCR system for Sheba recognition of character depend on neural network is presented. After analysing various method for segmentation and pre-processing A and B which are used for image pre-processing and segmentation respectively to enhance performance for projected framework. All the issues and challenges for OCR system and relevancy or accuracy of proposed method are discussed and analysed briefly

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Section: Language Backgroundmentioning

confidence: 99%

Section: Historical View Of Ocrmentioning

confidence: 99%

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Recognition of Handwritten Sheba Character using Artificial Neural Network

2021

IJATCSE

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“…Research Gap Analysis: many research and experiment of OCR is carried forward with convolutional network and recurrent neural network (RNN) which has an upper hand towards finding patterns in images but it takes lot of data and computation resource for training the model [6]- [8]. Siamese model uses far less data than that of traditional convolutional neural network (CNN) and RNN and very few Int J Artif Intell ISSN: 2252-8938  research has been done with OCR with Siamese network.…”

Section: Introductionmentioning

confidence: 99%

Optically processed Kannada script realization with Siamese neural network model

Parathra Sreedharanpillai,

Abraham,

Kotapuzakal Varghese

2024

IJ-AI

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<p><span>Optical character recognition (OCR) is a technology that allows computers to recognize and extract text from images or scanned documents. It is commonly used to convert printed or handwritten text into machine-readable format. This Study presents an OCR system on Kannada Characters based on siamese neural network (SNN). Here the SNN, a Deep neural network which comprises of two identical convolutional neural network (CNN) compare the script and ranks based on the dissimilarity. When lesser dissimilarity score is identified, prediction is done as character match. In this work the authors use 5 classes of Kannada characters which were initially preprocessed using grey scaling and convert it to pgm format. This is directly input into the Deep convolutional network which is learnt from matching and non-matching image between the CNN with contrastive loss function in Siamese architecture. The Proposed OCR system uses very less time and gives more accurate results as compared to the regular CNN. The model can become a powerful tool for identification, particularly in situations where there is a high degree of variation in writing styles or limited training data is available.</span></p>

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“…Eventually histogram-of-oriented gradient for features and neural network for classification was employed [24]. Lately, modified AlexNet model with batch normalization was proposed [25] for Kannada character recognition. An effective scene text detection method was proposed [26] which involves connected component extraction, character linking and text/non-text classification.…”

Section: Introductionmentioning

confidence: 99%

AksharaNet: A GPU Accelerated Modified Depth-Wise Separable Convolution for Kannada Text Classification

Siddiqua¹,

Naveena²,

Manvi³

et al. 2021

RIA

Self Cite

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For content-based indexing and retrieval applications, text characters embedded in images are a rich source of information. Owing to their different shapes, grayscale values, and dynamic backgrounds, these text characters in scene images are difficult to detect and classify. The complexity increases when the text involved is a vernacular language like Kannada. Despite advances in deep learning neural networks (DLNN), there is a dearth of fast and effective models to classify scene text images and the availability of a large-scale Kannada scene character dataset to train them. In this paper, two key contributions are proposed, AksharaNet, a graphical processing unit (GPU) accelerated modified convolution neural network architecture consisting of linearly inverted depth-wise separable convolutions and a Kannada Scene Individual Character (KSIC) dataset which is grounds-up curated consisting of 46,800 images. From results it is observed AksharaNet outperforms four other well-established models by 1.5% on CPU and 1.9% on GPU. The result can be directly attributed to the quality of the developed KSIC dataset. Early stopping decisions at 25% and 50% epoch with good and bad accuracies for complex and light models are discussed. Also, useful findings concerning learning rate drop factor and its ideal application period for application are enumerated.

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Recognition of Kannada Characters in Scene Images using Neural Networks

Cited by 7 publications

References 17 publications

Recognition of Handwritten Sheba Character using Artificial Neural Network

Recognition of Handwritten Sheba Character using Artificial Neural Network

Optically processed Kannada script realization with Siamese neural network model

AksharaNet: A GPU Accelerated Modified Depth-Wise Separable Convolution for Kannada Text Classification

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