Face Recognition using Probabilistic Neural Networks

n this paper we address the problem of face detection and recognition of grey scale frontal view images. We propose a face recognition system based on probabilistic neural networks (PNN) architecture. The system is implemented using voronoi/ delaunay tessellations and template matching. Images are segmented successfully into homogeneous regions by virtue of voronoi diagram properties. Face verification is achieved using matching scores computed by correlating edge gradients of reference images. The advantage of classification using PNN models is its short training time. The correlation based template matching guarantees good classification results.

Speech Recognition of Malayalam Numbers

Digit speech recognition is important in many applications such as automatic data entry, PIN entry, voice dialing telephone, automated banking system, etc. This paper presents speaker independent speech recognition system for Malayalam digits. The system employs Mel frequency cepstrum coefficient (MFCC) as feature for signal processing and Hidden Markov model (HMM) for recognition. The system is trained with 21 male and female voices in the age group of 20 to 40 years and there was 98.5% word recognition accuracy (94.8% sentence recognition accuracy) on a test set of continuous digit recognition task.

Handwritten Character Recognition of South Indian Scripts: A Review

Handwritten character recognition is always a frontier area of research in the field of pattern recognition and image processing and there is a large demand for OCR on hand written documents. Even though, sufficient studies have performed in foreign scripts like Chinese, Japanese and Arabic characters, only a very few work can be traced for handwritten character recognition of Indian scripts especially for the South Indian scripts. This paper provides an overview of offline handwritten character recognition in South Indian Scripts, namely Malayalam, Tamil, Kannada and Telungu

Unconstrained Handwritten Malayalam Character Recognition using Wavelet Transform and Support vector Machine Classifier

This paper presents the application of wavelet processing in the domain of handwritten character recognition. To attain high recognition rate, robust feature extractors and powerful classifiers that are invariant to degree of variability of human writing are needed. The proposed scheme consists of two stages: a feature extraction stage, which is based on Haar wavelet transform and a classification stage that uses support vector machine classifier. Experimental results show that the proposed method is effective

Offline Handwritten Malayalam Character Recognition Based on Chain Code Histogram

Optical Character Recognition plays an important role in Digital Image Processing and Pattern Recognition. Even though ambient study had been performed on foreign languages like Chinese and Japanese, effort on Indian script is still immature. OCR in Malayalam language is more complex as it is enriched with largest number of characters among all Indian languages. The challenge of recognition of characters is even high in handwritten domain, due to the varying writing style of each individual. In this paper we propose a system for recognition of offline handwritten Malayalam vowels. The proposed method uses Chain code and Image Centroid for the purpose of extracting features and a two layer feed forward network with scaled conjugate gradient for classification

A System for Offline Recognition of Handwritten Characters in Malayalam Script

In this paper, we propose a handwritten character recognition system for Malayalam language. The feature extraction phase consists of gradient and curvature calculation and dimensionality reduction using Principal Component Analysis. Directional information from the arc tangent of gradient is used as gradient feature. Strength of gradient in curvature direction is used as the curvature feature. The proposed system uses a combination of gradient and curvature feature in reduced dimension as the feature vector. For classification, discriminative power of Support Vector Machine (SVM) is evaluated. The results reveal that SVM with Radial Basis Function (RBF) kernel yield the best performance with 96.28% and 97.96% of accuracy in two different datasets. This is the highest accuracy ever reported on these datasets

Development & evaluation of different acoustic models for Malayalam continuous speech recognition

Performance of any continuous speech recognition system is dependent on the accuracy of its acoustic model. Hence, preparation of a robust and accurate acoustic model lead to satisfactory recognition performance for a speech recognizer. In acoustic modeling of phonetic unit, context information is of prime importance as the phonemes are found to vary according to the place of occurrence in a word. In this paper we compare and evaluate the effect of context dependent tied (CD tied) models, context dependent (CD) and context independent (CI) models in the perspective of continuous speech recognition of Malayalam language. The database for the speech recognition system has utterance from 21 speakers including 11 female and 10 males. Our evaluation results show that CD tied models outperforms CI models over 21%.

Malayalam Isolated Digit Recognition using HMM and PLP cepstral coefficient

Development of Malayalam speech recognition system is in its infancy stage; although many works have been done in other Indian languages. In this paper we present the first work on speaker independent Malayalam isolated speech recognizer based on PLP (Perceptual Linear Predictive) Cepstral Coefficient and Hidden Markov Model (HMM). The performance of the developed system has been evaluated with different number of states of HMM (Hidden Markov Model). The system is trained with 21 male and female speakers in the age group ranging from 19 to 41 years. The system obtained an accuracy of 99.5% with the unseen data

Connected digit speech recognition system for Malayalam language

A connected digit speech recognition is important in many applications such as automated banking system, catalogue-dialing, automatic data entry, automated banking system, etc. This paper presents an optimum speaker-independent connected digit recognizer forMalayalam language. The system employs Perceptual Linear Predictive (PLP) cepstral coefficient for speech parameterization and continuous density Hidden Markov Model (HMM) in the recognition process. Viterbi algorithm is used for decoding. The training data base has the utterance of 21 speakers from the age group of 20 to 40 years and the sound is recorded in the normal office environment where each speaker is asked to read 20 set of continuous digits. The system obtained an accuracy of 99.5 % with the unseen data.

A Comparative study of HMM and SVM in Malayalam Digit Recognition

A primary medium for the human beings to communicate through language is Speech. Automatic Speech Recognition is wide spread today. Recognizing single digits is vital to a number of applications such as voice dialling of telephone numbers, automatic data entry, credit card entry, PIN (personal identification number) entry, entry of access codes for transactions, etc. In this paper we present a comparative study of SVM (Support Vector Machine) and HMM (Hidden Markov Model) to recognize and identify the digits used in Malayalam speech.

Pattern Analysis Techniques for the Recognition of Unconstrained Handwritten Malayalam Character Images

The objective of the study is to develop a hand written character recognition system that could recognisze all the characters in the mordern script of malayalam language at a high recognition rate

On-Line Handwritten Character Recognition using Kohonen Networks

This paper presents an efficient Online Handwritten character Recognition System for Malayalam Characters (OHR-M) using Kohonen network. It would help in recognizing Malayalam text entered using pen-like devices. It will be more natural and efficient way for users to enter text using a pen than keyboard and mouse. To identify the difference between similar characters in Malayalam a novel feature extraction method has been adopted-a combination of context bitmap and normalized (x, y) coordinates. The system reported an accuracy of 88.75% which is writer independent with a recognition time of 15-32 milliseconds

HelF, a suppressor of RNAi mediated gene silencing in Dictyostelium discoideum

RNA interference (RNAi) is a recently discovered process, in which double stranded RNA (dsRNA) triggers the homology-dependant degradation of cognate messenger RNA (mRNA). In a search for new components of the RNAi machinery in Dictyostelium, a new gene was identified, which was called helF. HelF is a putative RNA helicase, which shows a high homology to the helicase domain of Dicer, to the helicase domain of Dictyostelium RdRP and to the C. elegans gene drh-1, that codes for a dicer related DExH-box RNA helicase, which is required for RNAi. The aim of the present Ph.D. work was to investigate the role of HelF in PTGS, either induced by RNAi or asRNA. A genomic disruption of the helF gene was performed, which resulted in a distinct mutant morphology in late development. The cellular localization of the protein was elucidated by creating a HelF-GFP fusion protein, which was found to be localized in speckles in the nucleus. The involvement of HelF in the RNAi mechanism was studied. For this purpose, RNAi was induced by transformation of RNAi hairpin constructs against four endogenous genes in wild type and HelF- cells. The silencing efficiency was strongly enhanced in the HelF K.O. strain in comparison with the wild type. One gene, which could not be silenced in the wild type background, was successfully silenced in HelF-. When the helF gene was disrupted in a secondary transformation in a non-silenced strain, the silencing efficiency was strongly improved, a phenomenon named here “retrosilencing”. Transcriptional run-on experiments revealed that the enhanced gene silencing in HelF- was a posttranscriptional event, and that the silencing efficiency depended on the transcription levels of hairpin RNAs. In HelF-, the threshold level of hairpin transcription required for efficient silencing was dramatically lowered. The RNAi-mediated silencing was accompanied by the production of siRNAs; however, their amount did not depend on the level of hairpin transcription. These results indicated that HelF is a natural suppressor of RNAi in Dictyostelium. In contrast, asRNA mediated gene silencing was not enhanced in the HelF K.O, as shown for three tested genes. These results confirmed previous observations (H. Martens and W. Nellen, unpublished) that although similar, RNAi and asRNA mediated gene silencing mechanisms differ in their requirements for specific proteins. In order to characterize the function of the HelF protein on a molecular level and to study its interactions with other RNAi components, in vitro experiments were performed. Besides the DEAH-helicase domain, HelF contains a double-stranded RNA binding domain (dsRBD) at its N-terminus, which showed high similarity to the dsRBD domain of Dicer A from Dictyostelium. The ability of the recombinant dsRBDs from HelF and Dicer A to bind dsRNA was examined and compared. It was shown by gel-shift assays that both HelF-dsRBD and Dicer-dsRBD could bind directly to long dsRNAs. However, HelF-dsRBD bound more efficiently to dsRNA with imperfect matches than to perfect dsRNA. Both dsRBDs bound specifically to a pre-miRNA substrate (pre-let-7). The results suggested that most probably there were two binding sites for the proteins on the pre-miRNA substrate. Moreover, it was shown that HelF-dsRBD and Dicer-dsRBD have siRNA-binding activity. The affinities of the two dsRBDs to the pre-let-7 substrate were also examined by plasmon surface resonance analyses, which revealed a 9-fold higher binding affinity of the Dicer-dsRBD to pre-let-7 compared to that of the HelF-dsRBD. The binding of HelF-dsRBD to the pre-let-7 was impaired in the presence of Mg2+, while the Dicer-dsRBD interaction with pre-let-7 was not influenced by the presence of Mg2+. The results obtained in this thesis can be used to postulate a model for HelF function. In this, HelF acts as a nuclear suppressor of RNAi in wild type cells by recognition and binding of dsRNA substrates. The protein might act as a surveillance system to avoid RNAi initiation by fortuitous dsRNA formation or low abundance of dsRNA trigger. If the protein acts as an RNA helicase, it could unwind fold-back structures in the nucleus and thus lead to decreased RNAi efficiency. A knock-out of HelF would result in initiation of the RNAi pathway even by low levels of dsRNA. The exact molecular function of the protein in the RNAi mechanism still has to be elucidated. RNA interferenz (RNAi) ist ein in jüngster Zeit entdeckter Mechanismus, bei dem doppelsträngige RNA Moleküle (dsRNA) eine Homologie-abhängige Degradation einer verwandten messenger-RNA (mRNA) auslösen. Auf der Suche nach neuen Komponenten der RNAi-Maschinerie in Dictyostelium konnte ein neues Gen (helF) identifiziert werden. HelF ist eine putative RNA-Helikase mit einer hohen Homologie zur Helikasedomäne der bekannten Dicerproteine, der Helikasedomäne der Dictyostelium RdRP und zu dem C. elegans Gen drh-1, welches für eine Dicer-bezogene DExH-box RNA Helikase codiert, die am RNAi-Mechanismus beteiligt ist. Das Ziel dieser Arbeit war es, die Funktion von HelF im Zusammenhang des RNAi oder asRNA induzierten PTGS zu untersuchen. Es wurde eine Unterbrechung des helF-Gens auf genomischer Ebene (K.O.) vorgenommen, was bei den Mutanten zu einer veränderten Morphologie in der späten Entwicklung führte. Die Lokalisation des Proteins in der Zelle konnte mit Hilfe einer GFP-Fusion analysiert werden und kleinen Bereichen innerhalb des Nukleus zugewiesen werden. Im Weiteren wurde der Einfluss von HelF auf den RNAi-Mechanismus untersucht. Zu diesem Zweck wurde RNAi durch Einbringen von RNAi Hairpin-Konstrukten gegen vier endogene Gene im Wiltypstamm und der HelF--Mutante induziert. Im Vergleich zum Wildtypstamm konnte im HelF--Mutantenstamm eine stark erhöhte „Silencing“-Effizienz nachgewiesen werden. Ein Gen, welches nach RNAi Initiation im Wildtypstamm unverändert blieb, konnte im HelF--Mutantenstamm erfolgreich stillgelegt werden. Durch sekundäres Einführen einer Gendisruption im helF-Locus in einen Stamm, in welchem ein Gen nicht stillgelegt werden konnte, wurde die Effizienz des Stilllegens deutlich erhöht. Dieses Phänomen wurde hier erstmals als „Retrosilencing“ beschrieben. Mit Hilfe von transkriptionellen run-on Experimenten konnte belegt werden, dass es sich bei dieser erhöhten Stilllegungseffizienz um ein posttranskriptionelles Ereignis handelte, wobei die Stillegungseffizienz von der Transkriptionsstärke der Hairpin RNAs abhängt. Für die HelF--Mutanten konnte gezeigt werden, dass der Schwellenwert zum Auslösen eines effizienten Stillegens dramatisch abgesenkt war. Obwohl die RNAi-vermittelte Genstilllegung immer mit der Produktion von siRNAs einhergeht, war die Menge der siRNAs nicht abhängig von dem Expressionsniveau des Hairpin-Konstruktes. Diese Ergebnisse legen nahe, dass es sich bei der HelF um einen natürlichen Suppressor des RNAi-Mechanismus in Dictyostelium handelt. Im Gegensatz hierzu war die as-vermittelte Stilllegung von drei untersuchten Genen im HelF-K.O. im Vergleich zum Wildyp unverändert. Diese Ergebnisse bestätigten frühere Beobachtungen (H. Martens und W. Nellen, unveröffentlicht), wonach die Mechanismen für RNAi und asRNA-vermittelte Genstilllegung unterschiedliche spezifische Proteine benötigen. Um die Funktion des HelF-Proteins auf der molekularen Ebene genauer zu charakterisieren und die Interaktion mit anderen RNAi-Komponenten zu untersuchen, wurden in vitro Versuche durchgeführt. Das HelF-Protein enthält, neben der DEAH-Helikase-Domäne eine N-terminale Doppelstrang RNA bindende Domäne (dsRBD) mit einer hohen Ähnlichkeit zu der dsRBD des Dicer A aus Dictyostelium. Die dsRNA-Bindungsaktivität der beiden dsRBDs aus HelF und Dicer A wurde analysiert und verglichen. Es konnte mithilfe von Gel-Retardationsanalysen gezeigt werden, dass sowohl HelF-dsRBD als auch Dicer-dsRBD direkt an lange dsRNAs binden können. Hierbei zeigte sich, dass die HelF-dsRBD eine höhere Affinität zu einem imperfekten RNA-Doppelstrang besitzt, als zu einer perfekt gepaarten dsRNA. Für beide dsRBDs konnte eine spezifische Bindung an ein pre-miRNA Substrat nachgewiesen werden (pre-let-7). Dieses Ergebnis legt nah, dass es zwei Bindestellen für die Proteine auf dem pre-miRNA Substrat gibt. Überdies hinaus konnte gezeigt werden, dass die dsRBDs beider Proteine eine siRNA bindende Aktivität besitzen. Die Affinität beider dsRBDs an das pre-let-7 Substrat wurde weiterhin mit Hilfe der Plasmon Oberflächen Resonanz untersucht. Hierbei konnte eine 9-fach höhere Bindeaffinität der Dicer-dsRBD im Vergleich zur HelF-dsRBD nachgewiesen werden. Während die Bindung der HelF-dsRBD an das pre-let-7 durch die Anwesenheit von Mg2+ beeinträchtigt war, zeigte sich kein Einfluß von Mg2+ auf das Bindeverhalten der Dicer-dsRBD. Mit Hilfe der in dieser Arbeit gewonnen Ergebnisse lässt sich ein Model für die Funktion von HelF postulieren. In diesem Model wirkt HelF durch Erkennen und Binden von dsRNA Substraten als Suppressor von der RNAi im Kern. Das Protein kann als Überwachungsystem gegen eine irrtümliche Auslösung von RNAi wirken, die durch zufällige dsRNA Faltungen oder eine zu geringe Häufigkeit der siRNAs hervorgerufen sein könnte. Falls das Protein eine Helikase-Aktivität besitzt, könnte es rückgefaltete RNA Strukturen im Kern auflösen, was sich in einer verringerten RNAi-Effizienz wiederspiegelt. Durch Ausschalten des helF-Gens würde nach diesem Modell eine erfolgreiche Auslösung von RNAi schon bei sehr geringer Mengen an dsRNA möglich werden. Das Modell erlaubt, die exakte molekulare Funktion des HelF-Proteins im RNAi-Mechanismus weiter zu untersuchen.