Intelligent word recognition

About: Intelligent word recognition is a research topic. Over the lifetime, 2480 publications have been published within this topic receiving 45813 citations.

Review on OCR for Handwritten Indian Scripts Character Recognition

Abstract: Natural language processing and pattern recognition have been successfully applied to Optical Character Recognition (OCR). Character recognition is an important area in pattern recognition. Character recognition can be printed or handwritten. Handwritten character recognition can be offline or online. Many researchers have been done work on handwritten character recognition from the last few years. As compared to non-Indian scripts, the research on OCR of handwritten Indian scripts has not achieved that perfection. There are large numbers of systems available for handwritten character recognition for non-Indian scripts. But there is no complete OCR system is available for recognition of handwritten text in any Indian script, in general. Few attempts have been carried out on the recognition of Devanagari, Bangla, Tamil, Oriya and Gurmukhi handwritten scripts. In this paper, we presented a survey on OCR of these most popular Indian scripts.

A fast recognition system for isolated arabic characters

Abstract: This paper presents a very fast multi-stage algorithm for the recognition of non-Latin script Although the examples use Arabic script, the system could be adapted in minutes to deal with any character set, in particular non-Latin characters where no commercial OCR systems are available The approach used normalises isolated characters for size and extracts an image signature based on the number of black pixels in the rows and columns of the character and compares these values to a set of signatures for typical characters of the set This technique identifies not only the closet match but gives the closeness of match to all other characters in the set, which is expressed in a triangular confusion matrix

Discriminative training for HMM-based offline handwritten character recognition

Abstract: In this paper we report the use of discriminative training and other techniques to improve performance in a HMM-based isolated handwritten character recognition system. The discriminative training is maximum mutual information (MMI) training; we also improve results by using composite images which are the concatenation of the raw images, rotated and polar transformed versions of them; and we describe a technique called block-based principal component analysis (PCA). For effective discriminative training we need to increase the size of our training database, which we do by eroding and dilating the images to give a three-fold increase in training data. Although these techniques are tested using isolated Thai characters, both MMI and block-based PCA are applicable to the more difficult task of cursive handwriting recognition.

Using Hidden Markov Models as a Tool for Handwritten Text Line Segmentation

Abstract: In this paper, the segmentation of off-line cursive handwritten text lines into individual words is investigated. The problem is considered as a text line recognition task, adapted to the characteristics of segmentation. That is, at a certain position of a text line, it has to be decided whether the considered position belongs to a letter of a word, or to a space between two words. Thus the text line needs to be recognized as a sequence of non-space and space characters. For this purpose, three different recognizers based on hidden Markov models are designed, and results of writer-dependent as well as writer-independent experiments are reported in the paper.

HMM Based Online Handwritten Bangla Character Recognition Using Dirichlet Distributions

Abstract: A reasonably large database of online handwritten Bangla characters has been developed. Such a handwritten character sample is composed of one or more strokes. Seventy five such stroke classes have been identified on the basis of the varying handwriting styles present in the character database. Each character sample is a sequence of strokes emanating from these stroke classes. Another database of handwritten Bangla strokes has been developed from the character database. This is the first such database for Bangla script. Certain stroke level features are defined on the basis of certain extremum points which represent the stroke shape reasonably well. The proposed character classification method is a two-stage approach. First, a probability distribution is estimated for each stroke class using the stroke features and then an HMM based character classifier is designed using each stroke class as a state. The parameters of both the stroke class distributions and the character class HMMs are estimated on the basis of the training set having 29,951 character samples. The character level recognition accuracy obtained by the proposed method on the test set having 8,616 samples, is 91.85%.