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.

Fast classification of handwritten on-line Arabic characters

Abstract: Delaying the analysis launch until the completion of the handwritten word scribing, restricts on-line recognition systems to meet the highly responsiveness demands expected from such applications, and prevents implementing advanced features of input typing such as automatic word completion and real-time automatic spelling. This paper proposes an efficient Arabic handwritten characters recognizer aimed at facilitating real-time handwritten script analysis tasks. The fast classification is enabled by employing an efficient embedding of the feature vectors into a normed wavelet coefficients domain in which the Earth Movers Distance metric is approximated using the Manhattan distance. A sub-linear time character classification is achieved by utilizing metric indexing techniques. Using the results of the top ranked shapes of each predicted character, a list of candidate shapes of Arabic word parts is generated in a filter and refine approach to enable fast yet accurate recognition results in a dictionary-free environment. The system was trained and tested on characters and word parts extracted from the ADAB database, and promising accuracy and performance results were achieved.

Speeding up on-line recognition of handwritten characters by pruning the prototype set

Abstract: This work describes a prototype-based online handwritten character recognition system and a two-phase recognition scheme aimed to speed up the recognition. In the first phase, the prototype set is pruned and ordered on the basis of preclassification performed with heavily down-sampled characters and prototypes. In the second phase, the final classification is performed without down-sampling by using the reduced set of prototypes. Two down-sampling methods, a linear and nonlinear one, have been analyzed to see their properties regarding the recognition time and accuracy.

Off-line Nepali handwritten character recognition using Multilayer Perceptron and Radial Basis Function neural networks

Abstract: An off-line Nepali handwritten character recognition, based on the neural networks, is described in this paper. A good set of spatial features are extracted from character images. Accuracy and efficiency of Multilayer Perceptron (MLP) and Radial Basis Function (RBF) classifiers are analyzed. Recognition systems are tested with three datasets for Nepali handwritten numerals, vowels and consonants. The strength of this research is the efficient feature extraction and the comprehensive recognition techniques, due to which, the recognition accuracy of 94.44% is obtained for numeral dataset, 86.04% is obtained for vowel dataset and 80.25% is obtained for consonant dataset. In all cases, RBF based recognition system outperforms MLP based recognition system but RBF based recognition system takes little more time while training.

Region selection in handwritten character recognition using Artificial Bee Colony Optimization

Abstract: Detection of local regions with optimal discriminating information from a sample of handwritten character image is one of the most challenging tasks to the pattern recognition community. In order to identify such regions, the idea of Artificial Bee Colony Optimization has been utilized in the present work. The technique is evaluated to pin point the set of local regions offering optimal discriminating feature set for handwritten numeral and character recognition. Initially, 8 directional gradient features are extracted from every region of different levels of partitions created using a CG based Quad Tree partitioning approach. Then, using the present approach, at each level, sampling process is done based on support Vector Machine (SVM) in every single region. Applying the technique we have gained 33%, 14%, 9%, 19%interms of region reduction and 0.2%, 0.4%, 0%, 1.6% in terms of recognition for Arabic, Hindi, Telugu numerals and Bangla Basic character datasets respectively. Though the success rate has not improved significantly for all the datasets, sizable amount of reduction in regions has occurred for every dataset using the present technique. Thus the cost and time of feature extraction is reduced significantly without dropping the general recognition rate.