Mohammad Faidzul Nasrudin

Handwritten Cursive Jawi Character Recognition: A Survey

The subject of cursive handwritten character recognition is still open to be studied because of its complex nature. Recognition of Arabic handwritten and its variants such as Farsi (Persian) and Urdu had been receiving considerable attention in recent years. Numerous methods for recognizing Arabic characters have been proposed and applied to various types of images. These research highlights have not yet been given to another variant of Arabic which is the Jawi script. This paper provides a comprehensive review of existing works in handwritten Jawi character recognition. It includes the history and writing of Jawi, and challenges, current works, and future direction of Jawi character recognition system.

Starting configuration of Cuckoo Search algorithm using Centroidal Voronoi Tessellations

Cuckoo Search (CS) is a meta-heuristic optimization algorithm that is inspired by breeding strategy of some cuckoo species that involves laying of eggs in the nests of other host birds. Like other population based optimization algorithms, the initial positions of the population, in the case of CS are host nests, will influence the performance of the searching. Based on this fact, we believe that the CS algorithm can further be improved by strategically selecting the starting positions of the nests instead of the standard random selection. This work suggests the use of positions generated from the Centroidal Voronoi Tessellations (CVT) as the starting points for the nests. A CVT is a Voronoi tessellation of a set such that the generators of the Voronoi sets are simultaneously their centers of mass. The CVT will initially present the problem space in equally distributed manner. The performance of CS algorithm initialized using CVT is compared with those generated from the standard CS algorithm on several benchmark test functions. The results show that the initialization of CS algorithm using the CVT improves its performance especially for benchmark functions with high-dimensional input spaces.

Arabic calligraphy identification for Digital Jawi Paleography using triangle blocks

Digital Jawi Paleography is a field of research that helps paleographers to identify authors, origin and date of Jawi manuscripts. This research is important because of the existence of a huge amount of Malay manuscripts with unidentified authors, origin and date. Most researches in the area are for Roman and Hebrew text, whereas researches for Jawi text have just begun recently. In this paper, a novel technique is proposed in order to identify types of Arabic calligraphy in Malay ancient manuscripts that were written in Jawi. The novel technique is based on the triangle blocks that were adapted from scalene triangle. Twenty-one features have been extracted from the triangle blocks.

Irregular Rotation Deformation from Paper Scanning: An Investigation

Abstract Image acquisition has great influence on the performance of any computer vision application. Different methods can be utilized to acquire the digital image of a paper, whilst scanning scheme is among the most attractive methods. This attractiveness is because of the fewer types of potential deformations and the low cost of the scanning devices, e.g. flatbed scanners. However, paper is commonly placed imperfectly on the scanner. This slight rotation is not usually based on a pivot around the papers geometrical center (the well known regular rotation) but instead it is based on a pivot placed at the corner of the paper. Thus, the result is a digital image that is deformed with an “irregular rotation”. The characteristic of this deformation phenomenon is currently unknown to computer vision scientists. In this paper we provide an extensive investigation of this deformation. In addition, a new set of equations that sway and measure the transformation is proposed. Our investigation leads to the conclusion that the “irregular rotation” phenomenon produces a shear transformation. Furthermore, the experimental results confirm the theoretical findings.

Arabic calligraphy classification using triangle model for Digital Jawi Paleography analysis

Calligraphy classification of the ancient manuscripts gives useful information to paleographers. Researches on digital paleography using calligraphy are done on the manuscripts to identify unidentified place of origin, number of writers, and the date of ancient manuscripts. Information that are used are features from characters, tangent value and features known as Grey-Level Co-occurrence Matrix (GLCM). For Digital Jawi Paleography, a novel technique is proposed based on the triangle. This technique defines three important coordinates in the image of each character and translates it into triangle geometry form. The features are extracted from the triangle to represent the Jawi (Arabic writing in Malay language) characters. Experiments have been conducted using seven Unsupervised Machine Learning (UML) algorithms and one Supervised Machine Learning (SML). This stage focuses on the accuracy of Arabic calligraphy classification. Hence, the model and test data are Arabic calligraphy letters taken from calligraphy books. The number of model is 711 for the UML and 1019 for the SML. Twelve features are extracted from the formed triangles used.

Digital paleography: Using the digital representation of Jawi manuscripts to support paleographic analysis

Palaeography is the study of ancient handwritten manuscripts to date the age and to localize ancient and medieval scripts. It also deals with analysing the development of the letters shape. Ancient Jawi manuscripts are one of the least studiedarea. Nowadays, over 7789 known Jawi manuscripts are kept in custody of various libraries in Malaysia. Most of these manuscripts were undated with unknown authors and location of origin. Analysing the different types of writing styles and recognizing the manuscript illuminations can discover this important information. In this paper, we discuss the palaeographical analysis from the perspective of computer science and propose a general framework for that. This process involves investigation of Arabic influence on the Jawi manuscript writings, establishing the palaeographical type of the script, and classification of writing styles based on local and global Jawi image features.

Invariant Features from the Trace Transform for Jawi Character Recognition

The Trace transform, a generalization of the Radon transform, allows one to construct image features that are invariant to a chosen group of image transformations. It consists of tracing an image with straight lines along which certain functionals of the image function are calculated. It can be useful to construct invariant features to rotation, translation and scaling of the image. In this paper, we demonstrate the usefulness of the features in classification of Jawi character images using multilayer perceptron neural networks. We compare the result of character recognition with those obtained by using features based on affine moment invariants.

Offline Jawi handwritten recognizer using hybrid artificial neural networks and dynamic programming

This paper describes an offline Jawi handwritten recognizer using hybrid Artificial Neural Networks (ANN) as the character recognizer and Viterbi Dynamic Programming as verifier. We use a recognition-based segmentation approach to solve character segmentation problems. Segmented sub words images are segmented into a fixed width slices. The combinations of the slices form a segmentation graph. Two-layers of Back Propagation Neural Networks compute probabilities for each character hypotheses in the segmentation graph. Viterbi Dynamic Programming selects the maximum average probability of a character hypothesis combination from all possibility in segmentation graph. This system evaluates against selected word from a Jawi handwritten manuscripts. Recognition performance of the character in words presented.

Part-of-Speech for Old Malay Manuscript Corpus: A Review

Research in Malay Part-of-Speech (POS) has increased considerably in the past few years. From the literature, POS are known as the first stage in automated text analysis and the development of language technologies can scarcely begun without this initial phase. Malay language can be written in Roman or Jawi. Three different spelling between Roman and Jawi make this study essential. In this paper, we highlighted the problem and issues related to Malay language, POS general framework, POS approaches and techniques. POS at basis was introduced to get information from Old Malay Manuscripts that contain important information in various spheres of knowledge. Promising result for the auto-tagging of Malay written in Jawi is expected.

Handwritten Jawi words recognition using Hidden Markov Models

Handwritten Jawi recognition is a challenging task because of the cursive nature of the writing. In manuscript writings, words are writer-dependent. The recognition task of Jawi Manuscript still opens problem due to the existence of many difficulties, such as the variability of character shape, overlap and presence of ligature in manuscript words. This paper describes a technique of Jawi word recognition using Hidden Markov Model (HMM). The technique of segmentation-free method used to transform word image into sequences of frames. The geometrical features are extracted using sliding window from each observation frame sequence. Besides, baseline parameters of Jawi word are use in the calculation of black pixel density. Vector Quantization clusters these features and assigns them into symbols that will be used as HMM input. Experiments have been conducted on 579 images of 100 words lexicon of Syair Rakis manuscript, and the recognition rate has reached 84 percent recognition.