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 handwritten digit recognition using neural network

Abstract: Optical character recognition is a typical field of application of automatic classification methods. In this paper, we have introduced a whole new idea of recognition of isolated handwritten digits which is known to be a difficult task and still lacks a satisfactory technical solution. The present paper proposes a novel approach for recognition of handwritten digits i.e. neural network classification. Back propagation neural network is one of the simplest methods for training multilayer neural networks. In this paper, we designed a back propagated neural network and trained it with a set of handwritten digits. The average success rates of recognition of all digits are 91.2%.

Text recognition enhancement with a probabilistic lattice chart parser

Abstract: A probabilistic lattice chart parser is proposed for improving the performance of a text recognition technique. Digital images of words are recognized and alternatives for the identity of each are generated. Local word collocation statistics and a probabilistic chart parsing algorithm are used to determine the top N best parses for each sentence using the alternatives provided for the identity of each word by the recognition system. An approach in which text recognition and understanding are tightly integrated is discussed. An objective of this approach is to provide the capacity to process images of unrestricted English text. A large-scale lexicon, which supports the system, was acquired by training on corpora of over 3,000,000 words. The focus is on the implementation and performance of the probabilistic lattice chart parser. >

Off-line Recognition of Bad Quality Handwritten Words Using Prototypes

Abstract: This paper consists of three related parts. First, results of experiments on human perception which demonstrate potentials of text recognition are presented. Second, a representation technique for handwritten words is described which allows to construct word prototypes and apply them for recognition of bad quality words. Third, integration of information from multiple sources in word recognition and sentence recognition tasks is discussed. Results of experiments with cursive words and cheque amounts are also presented.

Shape Encoded Post Processing of Gurmukhi OCR

Abstract: A post-processor is an integral part of any OCR system. This paper proposes a method for detection and correction of errors in recognition results of handwritten and machine printed Gurmukhi OCR. Based on the shape similarity of characters, the consonants of Gurmukhi Script are divided into different sets. Each set is given a unique number. In case of a recognition error, based on the shape of the consonants, corrections are made by taking each consonant of the subset into consideration. According to proposed algorithm, each recognized word is first encoded based on its consonants. The corresponding code is then searched in the dictionary. If it exits then words from the list of the code are match with the source word. In case of match the word is treated as correct else suggestions are made based on the similarity of the source word with the words of the same code present in dictionary. The method has been tested on the output of OCR of variety of machine printed and handwritten documents.

Method and apparatus for recognizing characters

Abstract: A method and an apparatus for recognizing characters using an image are provided. A camera is activated according to a character recognition request and a preview mode is set for displaying an image photographed through the camera in real time. An auto focus of the camera is controlled and an image having a predetermined level of clarity is obtained for character recognition from the images obtained in the preview mode. The image for character recognition is character-recognition-processed so as to extract recognition result data. A final recognition character row is drawn that excludes non-character data from the recognition result data. A first word is combined including at least one character of the final recognition character row and a predetermined maximum number of characters. A dictionary database that stores dictionary information on various languages using the first word is searched, so as to provide the user with the corresponding word.