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.

A radical approach to handwritten Chinese character recognition using active handwriting models

Abstract: This paper applies active handwriting models (AHM) to handwritten Chinese character recognition. Exploiting active shape models (ASM), the AHM can capture the handwriting variation from character skeletons. The AHM has the following characteristics: principal component analysis is applied to capture variations caused by handwriting, an energy functional on the basis of chamfer distance transform is introduced as a criterion to fit the model to a target character skeleton, and the dynamic tunneling algorithm (DTA) is incorporated with gradient descent to search for shape parameters. The AHM is used within a radical approach to handwritten Chinese characters recognition, which converts the complex pattern recognition problem to recognizing a small set of primitive structures-radicals. Our initial experiments are conducted on 98 radicals covering 1400 loosely-constrained Chinese character categories written by 200 different writers. The correct matching rate is 94.2% on these 2.8/spl times/10/sup 5/ characters. Comparison with existing radical approaches shows that our method achieves superior performance.

Lexical post-processing optimization for handwritten word recognition

Abstract: 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 post-processing 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 features to deal with large vocabulary (creation of static sublexicon compressed in a tree structure); a dedicated string matching algorithm for online handwriting (to compensate for the recognition and the segmentation errors); and a specific exploration strategy of the results provided by the analytical word recognition process. Experimental results are reported using several lexicon sizes (about 1000, 7000 and 25000 entries) to evaluate different optimization strategies according to the recognition rate, computational cost and memory requirements.

Unconstrained handwritten character recognition using different classification strategies

Abstract: In this paper we tackle the problem of unconstrained handwritten character recognition using different classification strategies. For such an aim, four multilayer perceptron classifiers (MLP) are built and used into three different classification strategies: combination of two 26– class classifiers; a 26–metaclass classifier and a 52– class classifier. Experimental results on the NIST SD19 database show that better recognition performance is achieved by the metaclass classifier in which the uppercase and the lowercase representations of the characters are merged into single classes.

Investigating of Preprocessing Techniques and Novel Features in Recognition of Handwritten Arabic Characters

Abstract: There are many difficulties facing a handwritten Arabic recognition system such as unlimited variation in character shapes. This paper describes a new method for handwritten Arabic character recognition. We propose a novel efficient approach for the recognition of off-line Arabic handwritten characters. The approach is based on novel preprocessing operations, structural statistical and topological features from the main body of the character and also from the secondary components. Evaluation of the importance and accuracy of the selected features was made. Our method based on the selected features and the system was built, trained and tested by CENPRMI dataset. We used SVM (RBF) and KNN for classification to find the recognition accuracy. The proposed algorithm obtained promising results in terms of accuracy; with recognition rates of 89.2% for SVM. Compared with other related works and also our recently published work we find that our result is the highest among them.

Character recognition of Arabic and Latin scripts

Abstract: The goal to produce effective optical character recognition (OCR) methods has led to the development of a number of algorithms. The purpose of these is to take the handwritten or printed text and to translate it into a corresponding digital form. The multitude requirements and developments are well represented in the literature (I.S.I. Abuhaiba et al., 1994: C.Y. Suen, 1986). The primary objective of the paper is to provide an insight into a robust system which has been successfully developed and employed to recognise Latin and Arabic characters and whose workings has been described previoulsy (J. Cowell and F. Hussain, 2000). The focus is to discuss the main components used in the multi-stage system, paying particular attention to the normalisation process used for orientation and size for a given bitmapped character. The effectiveness of the approach is demonstrated through its workings for the Arabic and Latin case, both for characters and numbers.