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 comparative study of several modeling approaches for large vocabulary offline recognition of handwritten Chinese characters

Abstract: We compare three representative modeling approaches, namely the multiple-prototype-based template matching approach, the subspace approach and the continuous density hidden Markov model approach for large vocabulary, offline recognition of handwritten Chinese characters. On a task of classification of 4616 handwritten Chinese characters, we evaluate and compare the strength and weakness of individual approaches in terms of the classification accuracy, the memory requirement and the computational complexity. We offer recommendations for practitioners on how to make intelligent use of these modeling approaches for different purposes in different applications.

A holistic word recognition technique for handwritten Bangla words

Abstract: Holistic word recognition is the current trend for handwritten word recognition. The holistic paradigm in handwritten word recognition considers a word as a single, indivisible entity and attempts to recognise words from their overall shape unlike recognising the individual characters comprising the word. In the present work, concentric rectangles and convex hull-based features are designed in order to classify word images belonging to different classes. For the evaluation of the current technique, 2,754 handwritten Bangla word samples are collected from different sources. A neural network-based classifier is chosen on the basis of the performances of different classifiers and some statistical tests. The recognition performance of the technique is evaluated using a three-fold cross-validation method. From the experimental results, it is observed that the proposed technique correctly recognises 84.74% word images in best case.

Statistical language models for on-line handwritten sentence recognition

Abstract: This paper investigates the integration of a statistical language model into an on-line recognition system in order to improve word recognition in the context of handwritten sentences. Two kinds of models have been considered: n-gram and n-class models (with a statistical approach to create word classes). All these models are trained over the Susanne corpus and experiments are carried out on sentences from this corpus which were written by several writers. The use of a statistical language model is shown to improve the word recognition rate and the relative impact of the different language models is compared. Furthermore, we illustrate the interest to define an optimal cooperation between the language model and the recognition system to re-enforce the accuracy of the system.

A HMM–based System for Recognition of Handwritten Address Words

Abstract: This paper introduces a new handwriting recognition system that is currently under development. Our application is the reading of German handwritten addresses for automatic mail sorting. The quality of the handwritten words is often bad in this application, because writers are not very cooperative. Therefore we have developed some suitable and eecient preprocessing operations to clean the image and normalize the writing. Because the words are often diicult to segment into letters, we have chosen a segmentation{free approach for recognition with semi{continuous Hidden Markov Models. We are applying the technique of context modelling in a model hierarchy in order to train more speciic letter models. For training and evaluation, we have used a large sample of 15000 handwritten city and street names. A number of experiments have been performed to evaluate strategies for feature space reduction (Karhunen{Loeve transform, linear discrim-inant analysis). On a 100 word lexicon, we achieve recognition rates of up to 90% on large independent test sets.