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.

Offline Features for Classifying Handwritten Math Symbols with Recurrent Neural Networks

Abstract: In mathematical expression recognition, symbol classification is a crucial step. Numerous approaches for recognizing handwritten math symbols have been published, but most of them are either an online approach or a hybrid approach. There is an absence of a study focused on offline features for handwritten math symbol recognition. Furthermore, many papers provide results difficult to compare. In this paper we assess the performance of several well-known offline features for this task. We also test a novel set of features based on polar histograms and the vertical repositioning method for feature extraction. Finally, we report and analyze the results of several experiments using recurrent neural networks on a large public database of online handwritten math expressions. The combination of online and offline features significantly improved the recognition rate.

Recovery of temporal information of cursively handwritten words for on-line recognition

Abstract: On-line recognition differs from off-line recognition in that additional information about the drawing order of the strokes is available. This temporal information makes it easier to recognize handwritten texts with an on-line recognition system. In this paper we present a method for the recovery of the stroke order from static handwritten images. The algorithm was tested by classifying the words of an off-line database with a state-of-the-art on-line recognition system. On this database with 150 different words, written by four cooperative writers, a recognition rate of 97.4% was obtained.

Discriminatory power of handwritten words for writer recognition

Abstract: Analysis of allographs (characters) and allograph combinations (words) is the key for the identification/verification of a writer's handwriting. While allographs are usually part of words and the segmentation of a word into allographs is a subjective process, analysis of handwritten words is a natural option, complementary to allograph and document-level analysis. We consider four different types of features obtained using both segmentation-based and segmentation-free approaches: (i) GSC (gradient, structural and concavity) features that are extracted from the cells of a grid superimposed on the word image (ii) WMR (word model recognizer) features, extracted from the cells of superimposed grids on the segmented characters (iii) SC (shape curvature) features that describe characters by the distribution of curvature values on their contours and (iv) SCON (shape context) features that measure the similarity between character contour shapes. Their individual and accumulated performance is evaluated for the writer identification and verification tasks on over 75000 words images, written by more than 1000 writers. Experimental results show that handwritten words are very effective in discriminating handwriting and that both segmentation-free and segmentation-based approaches are valid.

Method of registering a character pattern into a user dictionary and a character recognition apparatus having the user dictionary

Abstract: In the present invention, an input handwritten character pattern is subjected to character recognition processing, and a recognition reliability of the character as a standard characteristic feature pattern is determined from the recognition result. If the recognition reliability is low, a warning is issued. In response to the warning, a user or operator can decide whether the character pattern should be registered in the user dictionary (106). If it is decided that the character pattern should be registered in the user dictionary, the character pattern is stored in the user dictionary with the information representing that the character pattern has low recognition reliability. When character patterns registered in the user dictionary are displayed on a screen, these characters are displayed in such a manner that it is possible to distinguish characters having low recognition reliability from characters having high recognition reliability. There is also provided a user name index file (5309) for storing information regarding characteristic features of a handwritten character pattern peculiar to a specific user. Furthermore, there is also provided a password input-and-decision part (5103) for making a decision of whether or not allow to access to the user dictionary based on the information of the handwritten character pattern input by a specific user.

Recognition of Off-Line Handwritten Arabic Words Using Neural Network

Abstract: Neural network (NN) have been used with some success in recognizing printed Arabic words. In this paper, a complete scheme for unconstrained Arabic handwritten word recognition based on a Neural network is proposed and discussed. The overall engine of this combination of a global feature scheme with a NN, is a system able to classify Arabic-Handwritten words of one hundred different writers. The system first attempts to remove some of the variation in the images that do not affect the identity of the handwritten word. Next, the system codes the skeleton and edge of the word so that feature information about the strokes in the skeleton is extracted. Then, a classification process based on the artificial NN classifier is used as global recognition engine, to classify the Arabic words. The output is a word in the dictionary. A detailed experiment is carried out, and successful recognition results are reported.