Recognition Of Ligatures Research Articles

Urdu Ligature Recognition System: An Evolutionary Approach

Cursive text recognition of Arabic script-based languages like Urdu is extremely complicated due to its diverse and complex characteristics. Evolutionary approaches like genetic algorithms have been used in the past for various optimization as well as pattern recognition tasks, reporting exceptional results. The proposed Urdu ligature recognition system uses a genetic algorithm for optimization and recognition. Overall the proposed recognition system observes the processes of pre-processing, segmentation, feature extraction, hierarchical clustering, classification rules and genetic algorithm optimization and recognition. The pre-processing stage removes noise from the sentence images, whereas, in segmentation, the sentences are segmented into ligature components. Fifteen features are extracted from each of the segmented ligature images. Intra-feature hierarchical clustering is observed that results in clustered data. Next, classification rules are used for the representation of the clustered data. The genetic algorithm performs an optimization mechanism using multi-level sorting of the clustered data for improving the classification rules used for recognition of Urdu ligatures. Experiments conducted on the benchmark UPTI dataset for the proposed Urdu ligature recognition system yields promising results, achieving a recognition rate of 96.72%.

Deep OCR for Arabic script‐based language like Pastho

AbstractDeveloping cursive script recognition systems have always been a challenging task for researchers. This article proposes a ligature‐based recognition system for the cursive Pashto script using four pre‐trained CNN models using a fine‐tuned approach. The SqueezeNet, ResNet, MobileNet and DenseNet models have been observed for the classification and the recognition of Pashto sub‐word (ligature). Overall, the proposed system is divided into two domains (Source and Target). The source domain contains the pre‐trained models used on the ImageNet Dataset. These models are later fine‐tuned using the transfer learning approach to be used for the Pashto ligature recognition. The data augmentation techniques of negative and contour are used to increase the representation of ligature images and the dataset size. The CNN models have been evaluated on the benchmarks Pashto ligatures FAST‐NU dataset. The proposed system achieved the highest recognition rate of up to 99.31% using the DenseNet architecture of Convolutional Neural Network for Pashto ligature.

Recognition of Nastaliq Urdu Text using Multi-SVM

Optical Character Recognition has emerged as an attractive research field nowadays. Lot of work has been done in Urdu script based on various approaches and diverse methodologies have been put forward based on Nastaliq font style. Urdu is written diagonally from top to bottom, the style known as Nastaliq. This feature of Nastaliq makes Urdu highly cursive and more sensitive leading to a difficult recognition problem. Due to the peculiarities of Nastaliq Style of writing, we have chosen ligature as a basic unit of recognition in order to reduce the complexity of system. The accuracy rate of recognizing ligature in Urdu text corresponds to the efficiency with which the ligatures are segmented. In addition to extracting connected components, the ligature segmentation takes into consideration various factors like baseline information, height, width, and centroid. In this paper ligature Recognition is performed by using multi-SVM (Sup-port Vector Machine) approach which gives an accuracy of 97% when 903 text images are fed to it.

Urdu-Text Detection and Recognition in Natural Scene Images Using Deep Learning

Urdu text is a cursive script and belongs to a non-Latin family of other cursive scripts like Arabic, Chinese, and Hindi. Urdu text poses a challenge for detection/localization from natural scene images, and consequently recognition of individual ligatures in scene images. In this paper, a methodology is proposed that covers detection, orientation prediction, and recognition of Urdu ligatures in outdoor images. As a first step, the custom FasterRCNN algorithm has been used in conjunction with well-known CNNs like Squeezenet, Googlenet, Resnet18, and Resnet50 for detection and localization purposes for images of size $320\times 240$ pixels. For ligature Orientation prediction, a custom Regression Residual Neural Network (RRNN) is trained/tested on datasets containing randomly oriented ligatures. Recognition of ligatures was done using Two Stream Deep Neural Network (TSDNN). In our experiments, five-set of datasets, containing 4.2K and 51K Urdu-text-embedded synthetic images were generated using the CLE annotation text to evaluate different tasks of detection, orientation prediction, and recognition of ligatures. These synthetic images contain 132, and 1600 unique ligatures corresponding to 4.2K and 51K images respectively, with 32 variations of each ligature (4-backgrounds and font 8-color variations). Also, 1094 real-world images containing more than 12k Urdu characters were used for TSDNN’s evaluation. Finally, all four detectors were evaluated and used to compare them for their ability to detect/localize Urdu-text using average-precision (AP). Resnet50 features based FasterRCNN was found to be the winner detector with AP of.98. While Squeeznet, Googlenet, Resnet18 based detectors had testing AP of.65,.88, and.87 respectively. RRNN achieved and accuracy of 79% and 99% for 4k and 51K images respectively. Similarly, for characters classification in ligatures, TSDNN attained a partial sequence recognition rate of 94.90% and 95.20% for 4k and 51K images respectively. Similarly, a partial sequence recognition rate of 76.60% attained for real world-images.

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

In the emerging age of technologies, machines are becoming more and more skilled and capable just like humans. Despite the fact that machines do not have their own intelligence, but still due to advancement in Artificial Intelligence (AI), machines are rapidly advancing. The area of Pattern Recognition (PR) deals with bringing enhancements to identify obscure patterns corresponding to specific classes. Optical Character Recognition (OCR) is a subfield of PR which deals with the recognition of characters. A great work has been done for Japanese, Hindi, Arabic and Chinese scripts, but only a diminutive work has been done for Urdu script. The Urdu language is highly cursive and is written in different calligraphic styles like Naskh, Nastalique, Kofi, Devani and Riqa. The Nastalique font is very calligraphic with aesthetic beauty. The ligature segmentation of Urdu Nastalique is also more difficult as compared to other languages. Urdu Nastalique has some characteristics like stacking of ligatures and cursiveness which makes its ligature segmentation a difficult task. Cursiveness means ligatures are joined together to form a new shape. It contains connected ligatures which makes it more complicated as compared to other languages. The ligature recognition of Urdu text by an OCR is a strenuous task due to variants of scaling, rotation, orientation and font style. In this study, a scale and rotation invariant classifier for Urdu Nastalique OCR is proposed. A combination of scale and location invariant moments is used for feature extraction and the classification is performed using Cascade Forward Backpropagation Neural Network. The model is validated through independent dataset testing and 5-fold cross-validation which gave 96.474% and 96.922% accuracy. The results depict the adaptability of the proposed model due to its high accuracy for recognition of Urdu Nastalique Ligature.

Two Stream Deep Neural Network for Sequence-Based Urdu Ligature Recognition

Urdu text is a complex cursive script and poses a challenge for recognition by OCR systems due to its large number of ligatures and cursive style. In literature, several techniques have been proposed to recognize Urdu ligatures. However, we have investigated that, suitable challenging datasets and the consequently higher recognition rate is needed for ligature recognition. In this paper, a hybrid model based on the holistic approach is adopted for the recognition of Urdu ligatures (compound characters). More than 3800 unique ligatures were used to generate 46K (38K training, 7K testing) synthetic ligatures with 9 different kinds of transformations along with the normal ligatures. Each ligature is processed through two streams of Deep Neural Networks, namely Alexnet and Vgg16 to obtain a unique set of features corresponding to each net. These features are fused and then used as an input to double layer Bidirectional Long Short Term (BLSTM) network for learning a model. The learned model maps ligature images to their corresponding sequence of individual Urdu characters. In the proposed methodology output is in the editable Urdu-script format. The proposed model was evaluated and have shown an accuracy of 97% on the training dataset and 80% on more than 7K parametrically different query ligatures (test-set).

Ligature categorization based Nastaliq Urdu recognition using deep neural networks

The cursive nature, Nastaliq writing style and a large number of different ligatures make ligature recognition very difficult in Urdu. In this paper, we present a segmentation-free approach to holistically recognize Urdu ligatures. We first generate a rich dataset which contains 17,010 ligatures with different orientation and different degrees of noise. Secondly, the ligatures are clustered (categorized) in order to reduce the search space and make the learning robust. Finally, we employ a deep neural network with dropout regularization to classify ligatures. The detailed experiments show that a deep neural network with dropout regularization and clustering of ligatures significantly enhances the classification accuracy.

Segmentation-free optical character recognition for printed Urdu text

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.

Offline Urdu OCR using Ligature based Segmentation for Nastaliq Script

There are two most popular writing styles of Urdu i.e. Naskh and Nastaliq. Considering Arabic OCR research, ample amount of work has been done on Naskh writing style; focusing on Urdu, which uses Arabic character set commonly used Nastaliq writing style. Due to Nastaliq writing style, Urdu OCR poses many distinct challenges like compactness, diagonal orientation and context character shape sensitivity etc., for OCR system to correctly recognize the Urdu text image. Due to compactness and slanting nature of Nastaliq writing style, existing methods for Naskh style would not give desirable results. Therefore, in this paper, we are presenting ligature based segmentation OCR system for Urdu Nastaliq script. We have discussed in detail various unique challenges for the Urdu OCR and different feature extraction techniques for Ligature recognition using SVM and kNN classifier. The system is trained to recognize 11,000 Urdu ligatures. We have achieved overall 90.29% accuracy tested on Urdu text images.