Pengecaman Tulisan Tangan Teksjawi Menggunakan Pengkelas Multiaras

Pengecaman tulisan tangan teks Jawi adalah satu tugas yang sangat mencabar di dalam bidang Pengecaman Aksara Optik (PAO) disebabkan Jawi adalah satu tulisan jenis bersambung. Tesis ini mengenegahkan teknik untuk memperbaiki kadar pengecaman teks Jawi tulisan tangan. Skema barn yang lebih cekap untuk prapemprosesan, penemberengan, penyarian fitur dan pemonnalan aksara, dan pengkelasan telah direka untuk memenuhi objektif tersebut. Dntuk prapemprosesan, kaedah pembetulan pencongan dan erotan menggunakan kaedah histogram orientasi cerunan (HOC) yang asalnya digunakan untuk dokumen Latin telah dimasukkan sebagai satu daripada modul prapemprosesan. Satu skema barn untuk penemberengan telah diperkenalkan. Ia berasaskan kepada gabungan kaedah unjuran profail histogram dan penentuan titik tembereng ubah suai (PIT) membentuk kaedah penentuan titik tembereng (PTT). Fitur-fitur disarikan daripada aksara yang telah ditemberengkan menggunakan tiga jenis fitur. Fitur-fitur ini ialah struktur, fitur Momen Tak-berubah (MTB) dan Taburan Pilrsel Hitam (TPH). Algoritma penyingkiran bahagian sekunder aksara Jawi (seperti titik-titik, A" " dan maddah) juga telah diperkenalkan supaya dapat mengelakkan daripada salah cam sekunder ini.Ia perlu dipisahkan terlebih dahulu sebelum melalui proses p'engecaman. Hal ini dapat mengurangkan bilangan kelas aksara Jawi daripada 124 kepada 60. Sebanyak 200 sampel setiap kelas aksara Jawi telah diujikan untuk tujuan pengkelasan. Dua aras sistem pengkelasan terdiri daripada Pengkelas Kumpulan berasaskan Ukuran Keserupaan (PKUK) dan Pengkelas berganda Genetik-Perambat-balik (PGPB). Di aras pertama, PKUK menggunakan fitur struktur dan MTB untuk mengelompokkan kesemua aksara. Tujuh jenis primitif diperoleh menggunakan fitur struktur, dan proses pengelompokan berdasarkan kepada jenis primitif ini. Fitur MTB digunakan untuk mengirakan ukuran keserupaan dan kemudian menentukan kadar pengkelasan untuk setiap kumpulan. Setelah kesemua sampel aksara telah dikelompokkan, PGPB digunakan untuk mengkelaskan setiap aksara dalam kumpulan masing-masing dan dilarikan secara berasingan. Kelas aksara yang terbanyak ialah 14 aksara. Di aras kedua, PGPB dilaksanakan dalam dua peringkat iaitu peringkat pembelajaran, dan peringkat ujian. Di peringkat pembelajaran, pengkelasan ini menggunakan fitur MTB dan TPH, manakala di peringkat ujian pengkelas ini menggunakan maklumat tambahan iaitu maklumat yang diperoleh ketika menyingkirkan juzuk sekunder, dan di samping fitur MTB serta TPH. Pemecahan masalah ini kepada dua aras telah mengurangkan masa pembelajaran yang diambil oleh pengkelas dan beIjaya menambah kadar pengecaman. Tesis ini membicarakan secara terperinci setiap algoritma dan prestasinya terhadap sampel yang digunakan didalam ujikaji. Perbandingan juga dibuat terhadap kaedah pengawalan pemberat PB menggunakan pendekatan Sifar, Rawak, serta Rawak Nguyen-Widrow, di samping pendekatan ubah suai AG. Prestasi menggunakan AG (ubah suai) memberikan hasil pengkelasan yang dijanjikan

Analisis Pengawalan Pemberat Rangkaian Neural Perambatan Balik untuk Pengecaman Aksara Jawi

One of the factors that influences the recognition ability of a neural network is the initial values given to the weight vector during the training phase. The network may be trapped into a local minima if the initial weights are not chosen carefully. This paper presents an analysis of the ability of the network to recognise Jawi characters after it was trained using different methods of weight initialization. Three most common methods are zero, random and Nguyen-Widrow random. This paper presents the effect of these three methods on the ability of the network's recognition

FRAMEWORK OF JAWI DIGITAL PALEOGRAPHY: A PRELIMINARY WORK

Paleography is the study of ancient handwritten manuscripts to date the age and to localize ancient and medieval scripts. It also deals with analyzing the development of the letters shape. Ancient Jawi manuscripts are one of the less studied. Nowadays, there are over 7789 known Jawi manuscripts were kept in custody of various libraries in Malaysia. Most of those manuscripts were undated with unknown author and location of origin. This important information can be discovered by analyzing the different type of writing styles and recognizing the manuscript illuminations. In this paper, we discuss the paleographical 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 paleographical type of the script, and classification of writing styles based on local and global Jawi image features

Rangkaian Neural Genetik Aplikasi dalam Pengecaman Aksara Jawi

Objektif asas bagi Algoritma Genetik (atau ringkasnya AG) ialah melihat proses evolusi asli dalam bentuk satu versi perisian. Ia sering digunakan untuk masalah pengoptimuman. Dalam proses ini suatu populasi boleh berkembang biak, ditot atau diklon, dan mati dalam beberapa saat. Perubahan ini berlaku secara berterusan. Kini, AG telah dikembangkan konsepnya ke dalam Rangkaian Neural (atau ringkasnya RN). Kertas ini membicarakan konsep atau proses evolusi yang digunakan didalam R

Twelve anchor points detection by direct point calculation

Facial features can be categorized it into three approaches; Region Approaches, Anchor Point (landmark) Approaches and Contour Approaches. Generally, anchor points approach provide more accurate and consistent representation. For this reason, anchor points approach has been chose to utilize. Although, as the experiment data sets have become larger, algorithms have become more sophisticated even if the reported recognition rates are not as high as in some earlier works. This will cause a higher complexity and computer burden. Indirectly, it also will affect the time for real time face recognition systems. Here, it is proposed the approach of calculating the points directly from the text file to detect twelve anchor points ( nose tip, mouth centre, right eye centre, left eye centre, upper nose and chin). In order to get the anchor points, points for the nose tip have to be detected first. Then the upper nose and face point is localization. Lastly, the outer and inner eyes corner is localized. An experiment has been carried out with 420 models taken from GavabDB in two positions with frontal view and variation of expressions and positions. Our results are compared with three researchers that is similar to and show that better result is obtained with a median error of the eight points is around 5.53mm

Jawi character recognition using the trace transform

The Trace transform, a generalisation of the Radon transform, allows one to construct image features that are invariant to a chosen group of image transformations. In this paper, we used some features, which are invariant to affine distortion, generated by the Trace transform to discriminate between Jawi characters. The process consists of tracing an image with straight lines, along which certain functionals of the image function are calculated, in all possible orientations. For each combination of functionals we derived a function of orientation of the tracing lines that is known as an object signature. If the functionals used have some predefined properties, this signature can be used to characterise the character in an affine way. We demonstrated the usefulness of the derived signature and compared the result of character recognition with those obtained by using features based on affine moment invariants. Experiments using the Trace transform produced decent results for the printed and handwritten Jawi character recognitions that are invariant to affine distortion.Keyword: Affine moment invariant; Jawi character recognition; trace transfor

Multi-Classifier Jawi Handwritten Sub-Word Recognition

The problems and challenges in Jawi handwritten recognition are inherited from Arabic script which consists of cursive natures, large variety of writing styles due to its morphologically rich, ligature, overlapping characters, dialects and the low quality of the manuscripts images. The word segmentation is difficult because the existence of sub words due to the presence of space within words when contain disconnect characters. The performance of previous Jawi handwritten recognition still consider sub-par. There are three main problem of previous approach. First, the recognizer consist of multiple independent components where the improvement of performance in one component not shared across the systems. Secondly, the features extraction using features engineering approach only works on specific subsets of training data and is less capable to handle broader variants of testing data. Finally, the classifier used implicit segmentation where target class is sub-word with limited lexicon. This paper propose use of Deep Learning approach to address the first problem where training is conducted end-to-end from input to class output which enable the improvement of each component to improve overall performance. Secondly, Convolutional Network is use as learning features optimizes the data representation through end-to-end training of the parameters from raw input data to target class. Finally, A multi-classifier implicitly segments the sub-word into sequences of characters are proposed. The classifiers consists of one sub-word length classifier and seven character classifiers. This approach is lexicon-free to address absent of lexicon data. Experiments conducted on a Jawi handwritten standard dataset showed an accuracy of up to 92.20% and suggest that the approach used is superior to state-of-the-art methods of Jawi handwriting recognition