Topic
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.
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TL;DR: A complete OCR system for Bangla, the second most popular script in the Indian subcontinent, is described, where more than three hundred character shapes are recognized by a combination of template and feature-matching approach.
Abstract: This paper considers optical character recognition (OCR) of Bangla, the second most popular script in the Indian subcontinent. A complete OCR system is described for documents of single Bangla font, where more than three hundred character shapes are recognized by a combination of template and feature-matching approach. Here the document image captured by a flatbed scanner is subject to tilt correction, line, word and character segmentation, simple and compound character separation, feature extraction and finally character recognition. Some character occurrence statistics have been computed to aid the recognition process. The simple character recognition is done by a feature-based tree classifier, and the compound character recognition involves a template matching approach preceded by a feature-based grouping. At present, recognition accuracy of about 96% is obtained by the system.
19 citations
11 Dec 1995
TL;DR: This paper presents an approach in which an on-line handwritten character is characterized by a sequence of dominant points in strokes and a sequences of writing directions between consecutive dominant points.
Abstract: In this paper we present an approach in which an on-line handwritten character is characterized by a sequence of dominant points in strokes and a sequence of writing directions between consecutive dominant points. The directional information is used for character preclassification and the positional information is used for fine classification. Doth preclassification and fine classification are based on dynamic programming matching. A recognition experiment has been conducted with 62 character classes of different writing styles and 21 people as data contributors. The recognition rate of this experiment is 91%, with 7.9% substitution rate and 1.1% rejection rate. The average processing time is 0.35 second per character on a 486 50MHz personal computer.
19 citations
TL;DR: The application of neural networks in recognizing characters from a printed script is explored and compared to traditional methods of generalization, a highly specific character set is trained for each type.
Abstract: the recent advances in the computing technology, many recognition tasks have become automated. Character Recognition maps a matrix of pixels into characters and words. Recently, artificial neural network theories have shown good capabilities in performing character recognition. In this paper, the application of neural networks in recognizing characters from a printed script is explored. Contrast to traditional methods of generalizing the character set, a highly specific character set is trained for each type. This can be termed as targeted character recognition.
19 citations
18 Aug 1997
TL;DR: New moment features for Chinese character recognition are proposed that provide significant improvements in terms of Chinese character Recognition, especially for those characters that are very close in shapes.
Abstract: Moment descriptors have been developed as features in pattern recognition since the moment method was first introduced. In this paper, new moment features for Chinese character recognition are proposed. These provide significant improvements in terms of Chinese character recognition, especially for those characters that are very close in shapes.
19 citations
01 Sep 2001-International Journal of Human-computer Studies \/ International Journal of Man-machine Studies
TL;DR: This paper proposes a non-keyboard computer interaction by using a write-pen or mouse to write Thai handwritten characters and words, using a feature-based, fuzzy logic and object-oriented approach (FBFLOOA) to recognize on-line handwritten Thai characters andWords.
Abstract: Normally, people use a keyboard to interact with a computer. This type of interaction has two main problems; typing speed and typing error. This paper proposes a non-keyboard computer interaction by using a write-pen or mouse to write Thai handwritten characters and words, using a feature-based, fuzzy logic and object-oriented approach (FBFLOOA) to recognize on-line handwritten Thai characters and words. The feature-based concept is used to extract handwritten character features, the fuzzy logic set is used to identify uncertain handwritten character shapes and the object-oriented approach is used to analyse, design and implement a handwritten character and word recognition program.Two phases of Thai handwritten character and word recognition are proposed. The first phase uses only the FBFLOOA to recognize a handwritten character and the second phase uses FBFLOOA combined with a Thai dictionary file to seek a correct answer for a rejected recognition character. The first phase experimental results show a recognition accuracy of 89.24%, 9.20% misrecognition and 1.56% rejection. The second phase precision results are 97.82%, 0.62% misrecognition and 1.56% rejection. Both phases have an average recognition speed of 6.72s per character. The FBFLOOA-executed program size is 189 KB and the Thai dictionary file is 853 KB, which makes FBFLOOA available for notebooks, mobile phones, calculators and pocket computers.
19 citations