Optical character recognition

About: Optical character recognition is a research topic. Over the lifetime, 7342 publications have been published within this topic receiving 158193 citations. The topic is also known as: OCR & optical character reader.

Automated conversion of engineering drawings to CAD form

Abstract: The authors present a discussion of the different aspects and requirements of the CAD conversion problem and describe the general architecture and algorithms of one commercially available CAD conversion system, the GTX 5000. Scanning, vectorization, text recognition, symbol recognition, context processing, and cleanup editing subsystems of the GTX 5000 are described. Several possible alternative approaches and algorithms are also compared. EPRI (Electric Power Research Institute)-funded enhancements are discussed, including neural networks for character and symbol recognition, touching and broken character processing, and text/symbol associativity. >

Method and apparatus for matching local self-similarities

Abstract: A method includes matching at least portions of first, second signals using local self-similarity descriptors of the signals. The matching includes computing a local self-similarity descriptor for each one of at least a portion of points in the first signal, forming a query ensemble of the descriptors for the first signal and seeking an ensemble of descriptors of the second signal which matches the query ensemble of descriptors. This matching can be used for image categorization, object classification, object recognition, image segmentation, image alignment, video categorization, action recognition, action classification, video segmentation, video alignment, signal alignment, multi-sensor signal alignment, multi-sensor signal matching, optical character recognition, image and video synthesis, correspondence estimation, signal registration and change detection. It may also be used to synthesize a new signal with elements similar to those of a guiding signal synthesized from portions of the reference signal. Apparatus is also included.

New Spatial-Gradient-Features for Video Script Identification

Abstract: In this paper, we present new features based on Spatial-Gradient-Features (SGF) at block level for identifying six video scripts namely, Arabic, Chinese, English, Japanese, Korean and Tamil This works helps in enhancing the capability of the current OCR on video text recognition by choosing an appropriate OCR engine when video contains multi-script frames The input for script identification is the text blocks obtained by our text frame classification method For each text block, we obtain horizontal and vertical gradient information to enhance the contrast of the text pixels We divide the horizontal gradient block into two equal parts as upper and lower at the centroid in the horizontal direction Histogram on the horizontal gradient values of the upper and the lower part is performed to select dominant text pixels In the same way, the method selects dominant pixels from the right and the left parts obtained by dividing the vertical gradient block vertically The method combines the horizontal and the vertical dominant pixels to obtain text components Skeleton concept is used to reduce pixel width to a single pixel to extract spatial features We extract four features based on proximity between end points, junction points, intersection points and pixels The method is evaluated on 770 frames of six scripts in terms of classification rate and is compared with an existing method We have achieved 821% average classification rate

Handwritten Hindi character recognition: a review

Abstract: As the years passed by, computers became more powerful and automation became the need of generation. Humans tried to automate their work and replace themselves with machines. This effort of transition from manual to automatic gave rise to various research fields, and document character recognition is one such field. From the last few years, there is a sincere contribution from researchers for the development of optical character recognition systems for various scripts and languages. As a result of intensive research and development, there has been a significant improvement in handwritten devnagari text recognition. The main focus of this study is detailed survey of existing techniques for recognition of offline handwritten Hindi characters. It addresses all the aspects of Hindi character recognition starting from database to various phases of character recognition. The most relevant techniques of preprocessing, feature extraction and classification are discussed in various sections of this study. Moreover, this study is a zest of work accepted and published by research community in recent years. This study benefits its readers by discussing limitations of existing techniques and by providing beneficial directions of research in this field.

ICDAR 2009 Handwritten Farsi/Arabic Character Recognition Competition

Abstract: In recent years, the recognition of Farsi and Arabic handwriting is drawing increasing attention. This paper describes the result of the ICDAR 2009 competition for handwritten Farsi/Arabic character recognition. To evaluate the submitted systems, we used large datasets containing both binary and gray-scale images. Many different groups downloaded the training sets; however, finally 4 systems successfully participated in the competition. The systems were tested on two known databases and one unknown dataset. Due to the similarity between some digits and characters in Farsi and Arabic, each recognizer was tested for digit and character sets separately. For benchmarking, only the recognition rates, as the most important characteristic, are considered. Since participants used different software and even operating systems, the relative recognition speed is not compared in this competition.