A Scale and Rotation Invariant Urdu Nastalique Ligature Recognition Using Cascade Forward Backpropagation Neural Network

Rehman, Khawaja Ubaid ur; Khan, Yaser Daanial

doi:10.1109/access.2019.2936363

Cited by 11 publications

(6 citation statements)

References 53 publications

(37 reference statements)

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“…Table III shows the performance comparison of the method [18] from the literature with the proposed method using publicly available benchmark data sets. We have implemented the method proposed in [18] to test its performance with a few of the benchmark data sets [7]. Even though we have implemented the method proposed in [18], the implementation may not meet the optimizations as per the expectations of the original author [16].…”

Section: Resultsmentioning

confidence: 99%

“…This paper reported an average recognition accuracy of 98.89% using four image data sets from the Brodatz database. K. U. Rehman et al in the paper [7] proposed a feature extraction technique for character recognition using existing moment based features such as raw moments, central moments, hu moments, and Zernike moments and reported a success rate of 96.922% using Urdu proprietary data set. L. A. Torres-Méndez et al, in the paper [8] presented a translation, rotation, and scale invariant method for object recognition by extracting topological object characteristics with the help of novel coding of the normalized moment of inertia.…”

Section: Related Workmentioning

confidence: 99%

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A Novel Geometrical Scale and Rotation Independent Feature Extraction Technique for Multi-lingual Character Recognition

Soora¹,

Mohammed²,

Mohammed³

2020

IJACSA

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This paper presents a novel geometrical scale and rotation independent feature extraction (FE) technique for multilingual character recognition (CR). The performance of any CR techniques mainly depends on the robustness of the proposed FE methods. Currently, there are very few scale and rotation independent FE techniques present in the literature which successfully extract the robust features from characters with noise such as distortion and breaks in the characters. Many FE methods from the literature failed to distinguish the characters which look similar in their appearance. So, in this paper, we have proposed a novel scale and rotation independent geometrical shape FE technique which successfully recognized distorted, broken, and similarly looking characters. Aside from the proposed FE technique, we've used crossing count (CC) features. Finally, we have combined the proposed features with CC features to make as Feature Vector (FV) of the character to be recognized. The proposed CR technique is evaluated using publicly available media-lab license plate (LP), ISI_Bengali, and Chars74K benchmark data sets and achieved encouraging results. To further assess the performance of the proposed FE method, we've used a proprietary data set containing nearly 168000 multilingual characters from English, Devanagari, and Marathi scripts and achieved encouraging results. We have observed better classification rates for the proposed FE method using publicly available benchmark data sets as compared to few of the CR FE methods from the literature.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Novel Geometrical Scale and Rotation Independent Feature Extraction Technique for Multi-lingual Character Recognition

Soora¹,

Mohammed²,

Mohammed³

2020

IJACSA

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show abstract

“…Henceforth, for furnishing the feature vector, only those mathematical and computational models are most apt which are sensitive to the relative positioning of component nucleotide bases within genomic sequences. It is a critical factor in formulating yielding and assiduous feature sets 18 – 26 . Since Hahn moments require two-dimensional data, therefore, the genomic sequences are converted into a two-dimensional notation S’ of size k*k which stores the same information as S but in a two-dimensional form such that and …”

Section: Methodsmentioning

confidence: 99%

Evaluating machine learning methodologies for identification of cancer driver genes

Malebary

Khan

2021

Sci Rep

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Cancer is driven by distinctive sorts of changes and basic variations in genes. Recognizing cancer driver genes is basic for accurate oncological analysis. Numerous methodologies to distinguish and identify drivers presently exist, but efficient tools to combine and optimize them on huge datasets are few. Most strategies for prioritizing transformations depend basically on frequency-based criteria. Strategies are required to dependably prioritize organically dynamic driver changes over inert passengers in high-throughput sequencing cancer information sets. This study proposes a model namely PCDG-Pred which works as a utility capable of distinguishing cancer driver and passenger attributes of genes based on sequencing data. Keeping in view the significance of the cancer driver genes an efficient method is proposed to identify the cancer driver genes. Further, various validation techniques are applied at different levels to establish the effectiveness of the model and to obtain metrics like accuracy, Mathew’s correlation coefficient, sensitivity, and specificity. The results of the study strongly indicate that the proposed strategy provides a fundamental functional advantage over other existing strategies for cancer driver genes identification. Subsequently, careful experiments exhibit that the accuracy metrics obtained for self-consistency, independent set, and cross-validation tests are 91.08%., 87.26%, and 92.48% respectively.

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“…Beberapa metode yang dapat digunakan untuk proses pengkalsifikasian tulisan dan fitur ekstraksi dalam machine learning yaitu seperti Support Vector Machine (SVM) dan Artificial Neural Network (ANN) [13]. Penelitian terkait Urdu Nastalique Ligature Recognition menggunakan Backpropagation Neural Network (BPP) [14], penelitian selanjutnya terkait Signature Recognition menggunakan metode ANN sebagai classifier [15], pengenalan karakter urdu script menggunakan metode BPP menghasilkan akurasi 92% [5] dan penelitian [16] terkait recognition of offline Tulu Handwritten Scripts juga menggunakan metode machine learning. Selain menggunakan metode machine learning, beberapa penelitian menggunakan pemrosesan yang lebih kompleks sehingga membutuhkan metode yang lebih advance yaitu deep learning.…”

Section: Pendahuluanunclassified

Komparasi Metode Backpropagation Neural Network dan Convolutional Neural Network Pada Pengenalan Pola Tulisan Tangan

Maharani

Saputra²,

Wirastuti³

2022

J-Cosine

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Historical manuscripts are one of documents that important to be preserved because they contain a lot of information, one example of them is script as the historical documents . Historical document mostly still use handwriting in so many reserch. Currently, there are many research regarding the preservation of characters. One way of preservation that can be used is the digitization process. Digitizing’s process tanable by recognizing existing information using technology. The technology that can be used is machine learning. Handwriting is a complex case because of the many variations of these characters and the output of the author where variations of the author will produce different writings. The relevant fields for text and documents are Optical Chacarter Recognition (OCR) and handwriting recognition. There are several methods that can be used in the machine learning process, including Artificial Neural Network (ANN) and Convolutional Neural Network (CNN). Both of these methods are methods that can accept complex image input to be processed and recognized, therefore this method is highly recommended for processing handwriting.

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A Scale and Rotation Invariant Urdu Nastalique Ligature Recognition Using Cascade Forward Backpropagation Neural Network

Cited by 11 publications

References 53 publications

A Novel Geometrical Scale and Rotation Independent Feature Extraction Technique for Multi-lingual Character Recognition

A Novel Geometrical Scale and Rotation Independent Feature Extraction Technique for Multi-lingual Character Recognition

Evaluating machine learning methodologies for identification of cancer driver genes

Komparasi Metode Backpropagation Neural Network dan Convolutional Neural Network Pada Pengenalan Pola Tulisan Tangan

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