Handwritten Chinese character recognition using spatial Gabor filters and self-organizing feature maps

Deng, Da; Chan, King Pan; Yu, Yinglin

doi:10.1109/icip.1994.413707

Cited by 16 publications

(10 citation statements)

References 10 publications

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“…Because of its superior mathematic properties, Gabor wavelet has been widely used for data compression [21], face recognition [25], texture analysis [16], handwriting recognition [4] and other image processing tasks in recent years. In OCR applications, Gabor wavelet has been applied to binary images [5], [6] and recently applied to a video stream [32].…”

Section: B Feature Extractionmentioning

confidence: 99%

Automatic Detection and Recognition of Signs From Natural Scenes

Chen

Yang

Zhang

et al. 2004

IEEE Trans. on Image Process.

232

105

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Abstract-In this paper, we present an approach to automatic detection and recognition of signs from natural scenes, and its application to a sign translation task. The proposed approach embeds multiresolution and multiscale edge detection, adaptive searching, color analysis, and affine rectification in a hierarchical framework for sign detection, with different emphases at each phase to handle the text in different sizes, orientations, color distributions and backgrounds. We use affine rectification to recover deformation of the text regions caused by an inappropriate camera view angle. The procedure can significantly improve text detection rate and optical character recognition (OCR) accuracy. Instead of using binary information for OCR, we extract features from an intensity image directly. We propose a local intensity normalization method to effectively handle lighting variations, followed by a Gabor transform to obtain local features, and finally a linear discriminant analysis (LDA) method for feature selection. We have applied the approach in developing a Chinese sign translation system, which can automatically detect and recognize Chinese signs as input from a camera, and translate the recognized text into English.

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Section: B Feature Extractionmentioning

confidence: 99%

Automatic Detection and Recognition of Signs From Natural Scenes

Chen

Yang

Zhang

et al. 2004

IEEE Trans. on Image Process.

232

105

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show abstract

“…Gabor filters are an effective and widely used tool for feature extraction that decompose geometry into local orientation and scale [22]. Their success in handwriting recognition [23] and printed character recognition [24] demonstrates their utility for this task. Using a minimally redundant design strategy [25], a bank of 18 Gabor filters spanning three scales (three full octaves) and six orientations (30° increments from 0° to 150°) is applied to the grayscale image x , yielding complex coefficients f that contain phase information.…”

Section: Markov Models For Recognitionmentioning

confidence: 99%

Scene Text Recognition Using Similarity and a Lexicon with Sparse Belief Propagation

Weinman

Learned-Miller

Hanson

2009

IEEE Trans. Pattern Anal. Mach. Intell.

161

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Scene text recognition (STR) is the recognition of text anywhere in the environment, such as signs and store fronts. Relative to document recognition, it is challenging because of font variability, minimal language context, and uncontrolled conditions. Much information available to solve this problem is frequently ignored or used sequentially. Similarity between character images is often overlooked as useful information. Because of language priors, a recognizer may assign different labels to identical characters. Directly comparing characters to each other, rather than only a model, helps ensure that similar instances receive the same label. Lexicons improve recognition accuracy but are used post hoc. We introduce a probabilistic model for STR that integrates similarity, language properties, and lexical decision. Inference is accelerated with sparse belief propagation, a bottom-up method for shortening messages by reducing the dependency between weakly supported hypotheses. By fusing information sources in one model, we eliminate unrecoverable errors that result from sequential processing, improving accuracy. In experimental results recognizing text from images of signs in outdoor scenes, incorporating similarity reduces character recognition error by 19%, the lexicon reduces word recognition error by 35%, and sparse belief propagation reduces the lexicon words considered by 99.9% with a 12X speedup and no loss in accuracy.

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“…Combined with a speech synthesizer, it can be used as an aid for people who are visually handicapped. As a result of intensive research and development efforts, systems are available for English language [5,6] Chinese/Japanese languages [7][8][9] and handwritten numerals [10,11] . However, less attention had been given to Indian language recognition.…”

Section: Introductionmentioning

confidence: 99%

An Improved Handwritten Tamil Character Recognition System using Octal Graph

Kannan¹,

Prabhakar²

2008

J. of Computer Science

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Problem Statement: Handwriting recognition has attracted voluminous research in recent times. The segmentation and recognition of the characters from handwritten scripts incorporates considerable overhead. Almost all the existing handwritten character recognition techniques use neural network approach, which requires lot of preprocessing and hence accomplishing these problems using neural network is a tedious task. Approach: In this study we propose a novel solution for performing character recognition in Tamil, the official language of the south Indian province of Tamil Nadu. Pursued by the preprocessing techniques, Segmentation, Normalization and Feature Extraction the approach utilizes octal graph conversion for recognizing off-line handwritten Tamil characters which improves the slant correction. The graph tries to represent the basic form of a letter independent of the style of writing. Using the weights of the graphs and by the appropriate feature matching with the predefined characters, the written characters are recognized. Results: The performance evaluation of off line handwritten Tamil character using octal graph conversion and the metrics based on ranks of the letters proves good Recognition Efficiency Conclusion: We show that, in practise, the proposed approach produces near optimal results besides outperforming the other methodologies in existence. Results indicate that the approach can be used for character recognition in other Indic scripts as well.

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Handwritten Chinese character recognition using spatial Gabor filters and self-organizing feature maps

Cited by 16 publications

References 10 publications

Automatic Detection and Recognition of Signs From Natural Scenes

Automatic Detection and Recognition of Signs From Natural Scenes

Scene Text Recognition Using Similarity and a Lexicon with Sparse Belief Propagation

An Improved Handwritten Tamil Character Recognition System using Octal Graph

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