Computer Science And Technology - Research Publications

Permanent URI for this collectionhttps://kr.cup.edu.in/handle/32116/82

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Author: search.filters.author.Misgar, Muzafar Mehraj

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    Recognition of offline handwritten Urdu characters using RNN and LSTM models
    (Springer, 2022-06-17T00:00:00) Misgar, Muzafar Mehraj; Mushtaq, Faisel; Khurana, Surinder Singh; Kumar, Munish
    Optical Character Recognition (OCR), helps to convert different types of scanned documents, such as images into searchable and editable content. OCR is language dependant and very limited research has been carried out in this field for Urdu and Urdu like scriptures (E.g. Farsi, Arabic, and Urdu) unlike other languages like English, Hindi, etc. The lack of research work is attributed to a lack of publically available benchmark databases and inherent complexities involved in these languages like cursive nature and change in the shape of a character depending upon its position in a ligature. Each character has 2�4 different shapes depending upon its position in the word; initial, medial, or final. In this article, the we have proposed a methodology to automate the data collection process and collected a large handwritten dataset of 110,785 Urdu characters and laid out the comaparative analysis of two deep learning models SimpleRNN and LSTM to showcase the potential of RNN models for chararacter recognition. Data was collected from 250 authors on the A4 size sheet. Each sheet contains 132 shapes for Urdu characters and 10 numerals. As far as the authors know, this is the first time that such a large dataset has been proposed which contains all the possible shapes of Urdu character numerals as well. Experimentation has been done for the numeral, full characters, and for whole data set separately to lay a comparative analysis of classification capabilities of RNN and LSTM models. Despite of such inherit complexities in Urdu script, the RNN and LSTM models proved to be more effective in achieving a high accuracy rates. Respective accuracy for RNN achieved for each category are: 96.96% for numerals, 85.22% for full characters and 73.62% for whole data and LSTM outperforms the prior one with max accuracy for each category of data as 97.80% for numerals, 97.43% for full characters and 91.30% for whole data. Besides, the proposed dataset opens a new window for future research, showcasing the huge potential of this dataset for data analysis not only for Urdu language but for other languages like Arabic, Persian,etc. which uses similar kind of character sets. � 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
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    UrduDeepNet: offline handwritten Urdu character recognition using deep neural network
    (Springer Science and Business Media Deutschland GmbH, 2021-06-07T00:00:00) Mushtaq, Faisel; Misgar, Muzafar Mehraj; Kumar, Munish; Khurana, Surinder Singh
    Handwritten Urdu character recognition system faces several challenges including the writer-dependent variations and non-availability of benchmark databases for cursive writing scripts. In this study, we propose a handwritten Urdu character dataset for Nasta�liq writing style covering isolated, positional characters as well as numerals. We also propose a convolutional neural network (CNN) architecture for the recognition of handwritten Urdu characters and numerals. CNN is a novel technique for image recognition that does not need explicit feature engineering and extraction and produces efficient results as compared to standard handcrafted feature extraction approaches. The proposed system was trained on a training dataset of 74, 285 samples and evaluated on a test dataset of 21, 223 samples and achieved a recognition rate of 98.82% for 133 classes, outperforming the results of all state-of-the-art systems for the Urdu language. � 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.