Turkish handwritten text recognition: a case of agglutinative languages

We describe a system for recognizing unconstrained Turkish handwritten text. Turkish has agglutinative morphology and theoretically an infinite number of words that can be generated by adding more suffixes to the word. This makes lexicon-based recognition approaches, where the most likely word is selected among all the alternatives in a lexicon, unsuitable for Turkish. We describe our approach to the problem using a Turkish prefix recognizer. First results of the system demonstrates the promise of this approach, with top-10 word recognition rate of about 40% for a small test data of mixed handprint and cursive writing. The lexicon-based approach with a 17,000 word-lexicon (with test words added) achieves 56% top-10 word recognition rate

Perkuatan Fondasi Telapak Dengan Turap

Reinforcement Foundation is a method to increase the capacity of supporters, so they can support the load of the building. This is required for buildings located on soft ground. Strengthening this foundation is also needed if the structure above will be increased so that the burden of building work increased. In this study, attempted to strengthen the foundations of the method by placing the sheet piles on the side of the foundation. Tests performed on two-dimensional model in the laboratory, by comparing the carrying capacity of the foundation with sheet piles and without sheet piles. Strengthening the foundation carried out with 3 (three) length variation of plaster that is: L / B = 0.75, L / B = 1.00 and L / B = 1.25. B, and 3 (three) variations in the location of plaster, which is in distance S / B = 0.5, S / B = 1.0 and S / B = 1.5. Test results showed that the installation of sheet piles can increase the capacity of foundation support. The results of this study showed the longer the higher the sheet piles supporting capacity building, with the results of 33%, 55% and (80% -100%), one each for L / B = 0.75; 1.00 and 1.25. However, increased capacity is not much influenced by the location of plaster especially for short plaster of L / B = 0,75 and L / B = 1,00. While for L / B =1,25, where the sheet piles getting close to the foundation, increase capacity increased as well

Pengenalan Pola Aksara Jawa Tulisan Tangan dengan Jaringan Syaraf Tiruan Perambatan-Balik

Automatic reading techniques of handwriting being developed. Computerization of Java script has started to be developed. However, there is no automated reader system for documents written in handwriting Java script. This research applies backpropagation neural network for recignition of handwritten java script. Preprosesing patterns done with Java script normalize the image size to be 40x40 pixels then feature extraction. Performance network training is 99.8% and performance examination is 95.81%

On-line Handwritten Character Recognition: An Implementation of Counterpropagation Neural Net

On-line handwritten scripts are usually dealt with pen tip traces from pen-down to pen-up positions. Time evaluation of the pen coordinates is also considered along with trajectory information. However, the data obtained needs a lot of preprocessing including filtering, smoothing, slant removing and size normalization before recognition process. Instead of doing such lengthy preprocessing, this paper presents a simple approach to extract the useful character information. This work evaluates the use of the counter- propagation neural network (CPN) and presents feature extraction mechanism in full detail to work with on-line handwriting recognition. The obtained recognition rates were 60% to 94% using the CPN for different sets of character samples. This paper also describes a performance study in which a recognition mechanism with multiple hresholds is evaluated for counter-propagation architecture. The results indicate that the application of multiple thresholds has significant effect on recognition mechanism. The method is applicable for off-line character recognition as well. The technique is tested for upper-case English alphabets for a number of different styles from different peoples

Recognition of Handwritten Azerbaijani Letters using Convolutional Neural Networks

Technology advancements have made it possible to fill out documents such as petitions and forms electronically. However, in some circumstances, hard copies of documents that are difficult to share, store, and save due to their rigid dimensions are still used to preserve documents in the conventional manner. It is crucial to convert these written documents into digital media because of this. From this view point, this goal of this study is to investigate various methods for the digitalization of handwritten documents. In this study, image processing methods were used to pre-process the documents that were converted to image format. These operations include splitting the image format of the document into the lines, separating them into words and characters, and then classifying the characters. Convolutional Neural Networks, which is used for image recognition, is one of the deep learning techniques used in classification. The Extended MNIST dataset and the symbol dataset created from the pre-existing documents are used to train the model. The success rate of the generated dataset was 88.72 percent

Segmentación de líneas de texto en documentos manuscritos antiguos independiente del lenguaje

Hasta el momento no se ha utilizado todo el conocimiento que hay en los manuscritos antiguos debido a que reconocimiento de texto manuscrito aún no cuenta con métodos robustos para esta tarea. El primer problema de los métodos para el reconocimiento de texto manuscrito es que requieren que el texto se encuentre dividido en líneas. Los métodos actuales para la segmentación de líneas de texto manuscrito no han sido optimizados para trabajar con manuscritos antiguos. La primera etapa de la Segmentación de Líneas de Texto (SLT) manuscrito consiste en la Localización de Líneas de Texto (LLT). Para la SLT se han propuesto métodos que buscan los valores máximos locales en un histograma. El problema para estos métodos es que existen demasiados máximos locales, lo cual no permite localizar las líneas que hay. La segunda etapa de la SLT en manuscritos antiguos consiste en la búsqueda de una ruta que permita separar las líneas de texto, el problema de los métodos actuales es que algunos realizan una búsqueda local de la ruta y los otros métodos buscan la ruta evitando pasar por la mayor cantidad de caracteres. En este trabajo se presenta un sistema compuesto por dos nuevos métodos para la LLT manuscrito y otro método para la Búsqueda de una Ruta que permita Segmentar Líneas de Texto en documentos manuscritos (BRSLT) que supera a los métodos analizados en el estado del arte en las dos etapas. En el primer método propuesto se presenta la extracción de un mapa de energía que incrementa las diferencias entre los máximos y mínimos locales en un histograma. El segundo método propuesto consiste en buscar la mejor ruta para segmentar líneas de texto manuscrito antiguo usando un algoritmo genético. Para evaluar la exactitud de los métodos propuestos se han realizado experimentos con dos colecciones de documentos. Se ha realizado una evaluación independiente de los dos métodos propuesto. Las colecciones de documentos incluyen los idiomas: español, chino, árabe, inglés, árabe-español con escritura moderna y escritura antigua. Con los resultados de la experimentación se ha demostrado que es posible mejorar la LLT implementando un mapa de energía que incremente las diferencias entre máximos y mínimos locales. Los experimentos de la segunda sección demuestran que es necesario realizar una optimización global de la ruta para segmentar líneas de texto

Multimodal biometrics scheme based on discretized eigen feature fusion for identical twins identification

The subject of twins multimodal biometrics identification (TMBI) has consistently been an interesting and also a valuable area of study. Considering high dependency and acceptance, TMBI greatly contributes to the domain of twins identification in biometrics traits. The variation of features resulting from the process of multimodal biometrics feature extraction determines the distinctive characteristics possessed by a twin. However, these features are deemed as inessential as they cause the increase in the search space size and also the difficulty in the generalization process. In this regard, the key challenge is to single out features that are deemed most salient with the ability to accurately recognize the twins using multimodal biometrics. In identification of twins, effective designs of methodology and fusion process are important in assuring its success. These processes could be used in the management and integration of vital information including highly selective biometrics characteristic possessed by any of the twins. In the multimodal biometrics twins identification domain, exemplification of the best features from multiple traits of twins and biometrics fusion process remain to be completely resolved. This research attempts to design a new scheme and more effective multimodal biometrics twins identification by introducing the Dis-Eigen feature-based fusion with the capacity in generating a uni-representation and distinctive features of numerous modalities of twins. First, Aspect United Moment Invariant (AUMI) was used as global feature in the extraction of features obtained from the twins handwritingfingerprint shape and style. Then, the feature-based fusion was examined in terms of its generalization. Next, to achieve better classification accuracy, the Dis-Eigen feature-based fusion algorithm was used. A total of eight distinctive classifiers were used in executing four different training and testing of environment settings. Accordingly, the most salient features of Dis-Eigen feature-based fusion were trained and tested to determine the accuracy of the classification, particularly in terms of performance. The results show that the identification of twins improved as the error of similarity for intra-class decreased while at the same time, the error of similarity for inter-class increased. Hence, with the application of diverse classifiers, the identification rate was improved reaching more than 93%. It can be concluded from the experimental outcomes that the proposed method using Receiver Operation Characteristics (ROC) considerably increases the twins handwriting-fingerprint identification process with 90.25% rate of identification when False Acceptance Rate (FAR) is at 0.01%. It is also indicated that 93.15% identification rate is achieved when FAR is at 0.5% and 98.69% when FAR is at 1.00%. The new proposed solution gives a promising alternative to twins identification application