Diagonal Based Feature Extraction for Handwritten Alphabets Recognition System using Neural Network

An off-line handwritten alphabetical character recognition system using multilayer feed forward neural network is described in the paper. A new method, called, diagonal based feature extraction is introduced for extracting the features of the handwritten alphabets. Fifty data sets, each containing 26 alphabets written by various people, are used for training the neural network and 570 different handwritten alphabetical characters are used for testing. The proposed recognition system performs quite well yielding higher levels of recognition accuracy compared to the systems employing the conventional horizontal and vertical methods of feature extraction. This system will be suitable for converting handwritten documents into structural text form and recognizing handwritten names

An online handwriting recognition system for Turkish

Despite recent developments in Tablet PC technology, there has not been any applications for recognizing handwritings in Turkish. In this paper, we present an online handwritten text recognition system for Turkish, developed using the Tablet PC interface. However, even though the system is developed for Turkish, the addressed issues are common to online handwriting recognition systems in general. Several dynamic features are extracted from the handwriting data for each recorded point and Hidden Markov Models (HMM) are used to train letter and word models. We experimented with using various features and HMM model topologies, and report on the effects of these experiments. We started with first and second derivatives of the x and y coordinates and relative change in the pen pressure as initial features. We found that using two more additional features, that is, number of neighboring points and relative heights of each point with respect to the base-line improve the recognition rate. In addition, extracting features within strokes and using a skipping state topology improve the system performance as well. The improved system performance is 94% in recognizing handwritten words from a 1000-word lexicon

Manuscript document digitalization and recognition: a first approach

The handwritten manuscript recognizing process belongs to a set of initiatives which lean to the preservation of cultural patrimony gathered in libraries and archives, where there exist a great wealth in documents and even handwritten cards that accompany incunabula books. This work is the starting point of a research and development project oriented to digitalization and recognition of manuscript materials. The paper presented here discuss different algorithms used in the first stage dedicated to image noise-cleaning in order to improve it before the character recognition process begins. In order to make the handwritten-text recognition and image digitalization process efficient, it must be preceded by a preprocessing stage of the image to be treated, which includes thresholding, noise cleaning, thinning, base-line alignment and image segmentation, among others. Each of these steps will allow us to reduce the injurious variability when recognizing manuscripts (noise, random gray levels, slanted characters, ink level in different zones), and so increasing the probability of obtaining a suitable text recognition. In this paper, two image thinning methods are considered, and implemented. Finally, an evaluation is carried out obtaining many conclusions related to efficiency, speed and requirements, as well as ideas for future implementations

Hierarchical approach for character recognition

This research mainly focuses on recognizing the handwritten character. Several efficient algorithms have been developed by us so far to separate: the handwritten characters from printed text character, the lines, the words, and each character. In this thesis, we concentrate on how to increase the efficiency of recognition of segmented handwritten characters. Certain characters share common features unique to each other, different from the rest of characters. These subsets of characters with common features are then further analyzed by the classifier thereby reducing the number of comparisons that are required. This results increasing the speed of character recognition

Manuscript document digitalization and recognition: a first approach

The handwritten manuscript recognizing process belongs to a set of initiatives which lean to the preservation of cultural patrimony gathered in libraries and archives, where there exist a great wealth in documents and even handwritten cards that accompany incunabula books. This work is the starting point of a research and development project oriented to digitalization and recognition of manuscript materials. The paper presented here discuss different algorithms used in the first stage dedicated to "image noise-cleaning" in order to improve it before the character recognition process begins. In order to make the handwritten-text recognition and image digitalization process efficient, it must be preceded by a preprocessing stage of the image to be treated, which includes thresholding, noise cleaning, thinning, base-line alignment and image segmentation, among others. Each of these steps will allow us to reduce the injurious variability when recognizing manuscripts (noise, random gray levels, slanted characters, ink level in different zones), and so increasing the probability of obtaining a suitable text recognition. In this paper, two image thinning methods are considered, and implemented. Finally, an evaluation is carried out obtaining many conclusions related to efficiency, speed and requirements, as well as ideas for future implementations.Proyecto de Enlace de Bibliotecas (PREBI

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