This work presents a baseline system used to evaluate feature sets for word recognition. The main goal is to determine an optimum feature set to represent the handwritten names for the months of the year in Brazilian Portuguese language. Three kinds of features are evaluated: perceptual, directional and topological. The evaluation shows that taken in isolation, the perceptual feature set produc...

Discrete Hidden Markov Model (HMM) and hybrid of Neural Network (NN) and HMM are popular methods in handwritten word recognition system. In the hybrid system, NN is used for character level recognition while HMM is used for producing word score based on the probability of the hypothesized characters combined. All reported results shows better recognition for the hybrid system due to better disc...

This paper introduces new database for Arabic handwritten words. The Arabic handwritten database (AHD/AMSH) represents a utility to facilitate the experiments of the character recognition algorithms. It contains three types of images: word, isolated character, and digit images. The AHD/AMSH can be used for baseline detection, characters segmentation, normalization, thinning, training and testin...

Recognition using only visual evidence cannot always be successful due to limitations of information and resources available during training. Considering relation among lexicon entries is sometimes useful for decision making. In this paper, we present a method to capture lexical similarity of a lexicon and reliability of a character recognizer which serve to capture the dynamism of the environm...

This paper brings a contribution to the problem of efficiently recognizing handwritten words from a limited size lexicon. For that, a multiple classifier system has been developed that analyzes the words from three different approximation levels, in order to get a computational approach inspired on the human reading process. For each approximation level a three-module architecture composed of a...

The research described in this paper focuses on the presentation of two novel preprocessing techniques for the task of off-line handwritten word recognition. A technique for the identification of straight and skewed underline noise is described along with a novel algorithm for detecting skew in handwritten words. The latter identifies skew by detecting the center of mass in each half of a word ...

Efficient preprocessing is very essential for automatic recognition of handwritten documents. In this paper, techniques on segmenting words in handwritten Arabic text are presented. Firstly, connected components (ccs) are extracted, and distances among different components are analyzed. The statistical distribution of this distance is then obtained to determine an optimal threshold for words se...

Handwritten Numeral recognition plays a vital role in postal automation services especially in countries like India where multiple languages and scripts are used Discrete Hidden Markov Model (HMM) and hybrid of Neural Network (NN) and HMM are popular methods in handwritten word recognition system. The hybrid system gives better recognition result due to better discrimination capability of the N...

A script independent character segmentation from word images technique has been reported here. Word to character segmentation is an important preprocessing step of optical character recognition process. But in case of handwritten text, presence of touching characters decreases the accuracy of the technique of the segmentation of the characters from the word. In this paper, segmentation of handw...

This paper presents a lexical post-processing optimization for handwritten word recognition. The aim of this work is to explore the combination of different lexical postprocessing approaches in order to optimize the recognition rate, the recognition time and memory requirements. The present method focuses on the following tasks: a lexicon organization with word filtering, based on holistic word...