Evaluation of cursive and non-cursive scripts using recurrent neural networks

Ahmed, Saad Bin; Naz, Saeeda; Razzak, Muhammad Imran; Rashid, Shiekh Faisal; Afzal, Muhammad Bilal; Breuel, Thomas M.

doi:10.1007/s00521-015-1881-4

Cited by 56 publications

(45 citation statements)

References 14 publications

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“…In most cases, the results have been reported on custom-developed databases making it difficult to objectively compare different systems. Comparison of our system can be carried out with [5,[16][17][18]22] and [5,10,13,19,21,28,29] on the basis of database used (UPTI) and recognition unit (ligatures), respectively. The recognition rates of these studies are summarized in Table 10.…”

Section: Results and Analysismentioning

confidence: 99%

“…The recognition rates of these studies are summarized in Table 10. It should however be noted that we employ ligatures as basic units of recognition as opposed to characters in [16][17][18]22]. Consequently, we report the ligature recognition rates while the implicit segmentation-based studies listed in Table 10 report character recognition rates.…”

Section: Results and Analysismentioning

confidence: 99%

“…Classification is carried using HMMs and less than 20 classes are considered in their study. The system evaluated on 1692 Urdu ligatures achieved A number of recent studies [16,22] have employed deep learning-based implicit segmentation techniques for recognition of cursive text leaving it to the classifier to implicitly find segmentation cue points of characters. Ahmed et al [22] applied Bidirectional Long Short-Term Memory (BLSTM) classifier to the character recognition of Urdu.…”

Section: Analytical Approachesmentioning

confidence: 99%

See 2 more Smart Citations

Segmentation-free optical character recognition for printed Urdu text

Din

Siddiqi

Khalid

et al. 2017

J Image Video Proc.

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This paper presents a segmentation-free optical character recognition system for printed Urdu Nastaliq font using ligatures as units of recognition. The proposed technique relies on statistical features and employs Hidden Markov Models for classification. A total of 1525 unique high-frequency Urdu ligatures from the standard Urdu Printed Text Images (UPTI) database are considered in our study. Ligatures extracted from text lines are first split into primary (main body) and secondary (dots and diacritics) ligatures and multiple instances of the same ligature are grouped into clusters using a sequential clustering algorithm. Hidden Markov Models are trained separately for each ligature using the examples in the respective cluster by sliding right-to-left the overlapped windows and extracting a set of statistical features. Given the query text, the primary and secondary ligatures are separately recognized and later associated together using a set of heuristics to recognize the complete ligature. The system evaluated on the standard UPTI Urdu database reported a ligature recognition rate of 92% on more than 6000 query ligatures.

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Section: Results and Analysismentioning

confidence: 99%

Section: Results and Analysismentioning

confidence: 99%

Section: Analytical Approachesmentioning

confidence: 99%

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Segmentation-free optical character recognition for printed Urdu text

Din

Siddiqi

Khalid

et al. 2017

J Image Video Proc.

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“…They reported 97.63 % accuracy on character recognition of Urdu language. Another work on offline handwritten Urdu recognition by BLSTM is proposed by [5]. They had taken samples from different users on plain paper and applied horizontal profile for text line segmentation.…”

Section: Literature Reviewmentioning

confidence: 99%

Balinese Character Recognition Using Bidirectional LSTM Classifier

Ahmed

Naz

Razzak

et al. 2016

Lecture Notes in Electrical Engineering

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The character recognition of cursive scripts always be provocative. The inherent challenges exists in cursive scripts captured researcher's interest to crop up the issues that surface in building a reliable OCR. There exists many ancient languages that require state of the art techniques to be applied on them. Every such language has its own inherent complex structure. We proposed Balinese character recognition system by Recurrent Neural Network (RNN) approach, so that their characteristics may get substantial attention from research community. The Balinese has Brahmic Indic ancestor having cursive writing style nearest to Devangri, Sinhala and Tamil. We employed BLSTM networks on Balinese character recognition.

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“…Ahmed et al [13] have employed a special type of recurrent neural network, called bidirectional long short-term memory (BLSTM) networks, to propose segmentation-free optical character recognition system. BLSTM has proven its efficiency in many research areas due to its ability to remember events when there are long time lags between events.…”

Section: Related Workmentioning

confidence: 99%

Printed Arabic Text Recognition using Linear and Nonlinear Regression

Shahin¹

2017

ijacsa

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Evaluation of cursive and non-cursive scripts using recurrent neural networks

Cited by 56 publications

References 14 publications

Segmentation-free optical character recognition for printed Urdu text

Segmentation-free optical character recognition for printed Urdu text

Balinese Character Recognition Using Bidirectional LSTM Classifier

Printed Arabic Text Recognition using Linear and Nonlinear Regression

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