uTHCD: A New Benchmarking for Tamil Handwritten OCR

Handwritten character recognition is a challenging research in the field of document image analysis over many decades due to numerous reasons such as large writing styles variation, inherent noise in data, expansive applications it offers, non-availability of benchmark databases etc. There has been considerable work reported in literature about creation of the database for several Indic scripts but the Tamil script is still in its infancy as it has been reported only in one database [5]. In this paper, we present the work done in the creation of an exhaustive and large unconstrained Tamil Handwritten Character Database (uTHCD). Database consists of around 91000 samples with nearly 600 samples in each of 156 classes. The database is a unified collection of both online and offline samples. Offline samples were collected by asking volunteers to write samples on a form inside a specified grid. For online samples, we made the volunteers write in a similar grid using a digital writing pad. The samples collected encompass a vast variety of writing styles, inherent distortions arising from offline scanning process viz stroke discontinuity, variable thickness of stroke, distortion etc. Algorithms which are resilient to such data can be practically deployed for real time applications. The samples were generated from around 650 native Tamil volunteers including school going kids, homemakers, university students and faculty. The isolated character database will be made publicly available as raw images and Hierarchical Data File (HDF) compressed file. With this database, we expect to set a new benchmark in Tamil handwritten character recognition and serve as a launchpad for many avenues in document image analysis domain. Paper also presents an ideal experimental set-up using the database on convolutional neural networks (CNN) with a baseline accuracy of 88% on test data.Comment: 30 pages, 18 figures, in IEEE Acces

Linear discriminant analysis : a detailed tutorial

Linear Discriminant Analysis (LDA) is a very common technique for dimensionality reduction problems as a preprocessing step for machine learning and pattern classification applications. At the same time, it is usually used as a black box, but (sometimes) not well understood. The aim of this paper is to build a solid intuition for what is LDA, and how LDA works, thus enabling readers of all levels be able to get a better understanding of the LDA and to know how to apply this technique in different applications. The paper first gave the basic definitions and steps of how LDA technique works supported with visual explanations of these steps. Moreover, the two methods of computing the LDA space, i.e. class-dependent and class-independent methods, were explained in details. Then, in a step-by-step approach, two numerical examples are demonstrated to show how the LDA space can be calculated in case of the class-dependent and class-independent methods. Furthermore, two of the most common LDA problems (i.e. Small Sample Size (SSS) and non-linearity problems) were highlighted and illustrated, and state-of-the-art solutions to these problems were investigated and explained. Finally, a number of experiments was conducted with different datasets to (1) investigate the effect of the eigenvectors that used in the LDA space on the robustness of the extracted feature for the classification accuracy, and (2) to show when the SSS problem occurs and how it can be addressed

Diagonal Fisher linear discriminant analysis for efficient face recognition

In this paper, a novel subspace method called diagonal Fisher linear discriminant analysis (DiaFLD) is proposed for face recognition. Unlike conventional principal component analysis and FLD, DiaFLD directly seeks the optimal projection vectors from diagonal face images without image-to-vector transformation. The advantage of the DiaFLD method over the standard 2-dimensional FLD (2DFLD) method is, the former seeks optimal projection vectors by interlacing both row and column information of images while the latter seeks the optimal projection vectors by using only row information of images. Our test results show that the DiaFLD method is superior to standard 2DFLD method and some existing well-known methods

(2D)2LDA: An efficient approach for face recognition

Although 2DLDA algorithm obtains higher recognition accuracy, a vital unresolved problem of 2DLDA is that it needs huge feature matrix for the task of face recognition. To overcome this problem, this paper presents an efficient approach for face image feature extraction, namely, (2D)2LDA method. Experimental results on ORL and Yale database show that the proposed method obtains good recognition accuracy despite having less number of coefficients

Some issues on choices of modalities for multimodal biometric systems

Biometrics-based authentication has advantages over other mechanisms, but there are several variabilities and vulnerabilities that need to be addressed. No single modality or combinations of modalities can be applied universally that is best for all applications. This paper deliberates different combinations of physiological biometric modalities with different levels of fusion. In our experiments, we have selected Face, Palmprint, Finger Knuckle Print, Iris, and Handvein modalities. All themodalities are of image type and publicly available, comprising at least 100 users. Proper selection of modalities for fusion can yield desired level of performance. Through our experiments it is learnt that a multimodal system which is considered just by increasing number of modalities by fusion would not yield the desired level of performance. Many alternate options for increased performance are presented

Multilingual OCR system for South Indian scripts and English documents: An approach based on Fourier transform and principal component analysis

Character recognition lies at the core of the discipline of pattern recognition where the aim is to represent a sequence of characters taken from an alphabet Kasturi, R., Gorman, L.O., Govindaraju, V., 2002. Document image analysis: a primer. Sadhana 27 (Part 1), 3–22. Though many kinds of features have been developed and their test performances on standard database have been reported, there is still room to improve the recognition rate by developing improved features. In this paper, we present a multilingual character recognition system for printed South Indian scripts (Kannada, Telugu, Tamil and Malayalam) and English documents. South Indian languages are most popular languages in India and around the world. The proposed multilingual character recognition is based on Fourier transform and principal component analysis (PCA), which are two commonly used techniques of image processing and recognition. PCA and Fourier transforms are classical feature extraction and data representation techniques widely used in the area of pattern recognition and computer vision. Our experimental results show the good performance over the data sets considered

Multimodal biometric fusion of face and palmprint at various levels

Recent years have witnessed researchers paying enormous attention to design efficient multi-modal biometric systems because of their ability to withstand spoof attacks. Single biometric sometimes fails to extract adequate information for verifying the identity of a person 7. On the other hand, by combining multiple modalities, enhanced performance reliability could be achieved. In this paper, we have fused face and palmprint modalities at all levels of fusion viz sensor level, feature level, decision level and score level. For this purpose, we have selected modality specific feature extraction algorithms for face and palmprint such as LDA and LPQ respectively. Popular databases AR (for face) and PolyU (for Palmprint) were considered for evaluation purposes. Rigorous experiments were conducted both under clean and noisy conditions to ascertain robust level of fusion and impact of fusion strategies at various levels of fusion for these two modalities. Results are substantiated with appropriate analysis