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.

Japanese OCR Error Correction using Character Shape Similarity and Statistical Language Model

Abstract: We present a novel OCR error correction method for languages without word delimiters that have a large character set, such as Japanese and Chinese. It consists of a statistical OCR model, an approximate word matching method using character shape similarity, and a word segmentation algorithm using a statistical language model. By using a statistical OCR model and character shape similarity, the proposed error corrector outperforms the previously published method. When the baseline character recognition accuracy is 90%, it achieves 97.4% character recognition accuracy.

Handwritten Bangla Compound character recognition: Potential challenges and probable solution.

Abstract: A novel technique is presented here for recognition of handwritten compound characters of Bangla alphabet. It advocates for incrementally expanding the number of learned character classes from more frequently occurred to less frequently occurred ones. The work is preceded by a survey for finding the frequencies of occurrences of all Bangla characters in the standard literature. One important finding of the survey is that only 4.27 percent of characters in a standard text piece are on average compound characters. Out of the 160 compound character classes, characters of 55 classes constitute 90 percent of the compound characters occurring on average in a standard text piece. For the time being, handwritten characters from these classes are considered here. The average recognition rate, as observed under this work, is 84.67 percent after 3 fold cross validation of results. It is more or less comparable with the performance reported in another related work[3]. The work presented here can be considered as an important step for the development of OCR for handwritten Bangla characters, including complex shaped compound characters.

Skeleton-Based Recognition of Chinese Calligraphic Character Image

Abstract: The large amount of digitized Chinese calligraphic works in existence is a valuable part of the Chinese cultural heritage. But they can hardly be recognized by optical character recognition (OCR) which performs well on machine printed characters against clean background, because there are so different styles of shape complexity characters. So the approaches of automatic Chinese calligraphic character recognition become more and more important. A novel skeletonization algorithm called MFITS (morphology-fused index table skeletonization) is proposed and a skeleton-based Chinese calligraphic character recognition method is proposed too. The experiments show that MFITS can extract skeletons with only a few deformations and the skeleton-based Chinese calligraphic character image recognition method has a good performance.

An Improved Handwritten Tamil Character Recognition System using Octal Graph

Abstract: 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.