Topic
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.
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18 Aug 1997
TL;DR: The proposed approach introduces a concept of table grid which can serve for advanced methods of table structure analysis, which provides a layer of terminal symbols for the table, which is used by syntactical methods.
Abstract: Algorithm for table image segmentation, a part of complete document recognition system is presented. The proposed approach introduces a concept of table grid which can serve for advanced methods of table structure analysis. It provides a layer of terminal symbols for the table, which is used by syntactical methods. Detailed discussion of grid detection is presented which is performed through the analysis of connected components projection profile. Simple rules for analysis of table structure cover majority of real life tables. The system is implemented, rested, and is now extensively used in FineReader OCR product.
44 citations
TL;DR: The article provides a review of the fundamental of neural networks and reports recent progress on Topics covered include dynamic modeling, model-based neural networks, statistical learning, eigenstructure-based processing, active learning, and generalization capability.
Abstract: The article provides a review of the fundamental of neural networks and reports recent progress. Topics covered include dynamic modeling, model-based neural networks, statistical learning, eigenstructure-based processing, active learning, and generalization capability. Current and potential applications of neural networks are also described in detail. Those applications include optical character recognition, speech recognition and synthesis, automobile and aircraft control, image analysis and neural vision, and several medical applications. Essentially, neural networks have become a very effective tool in signal processing, particularly in various recognition tasks.
44 citations
04 Feb 2013
TL;DR: This novel approach combines the OCR outputs from multiple thresholded images by aligning the text output and producing a lattICE of word alternatives from which a lattice word error rate (LWER) is calculated.
Abstract: For noisy, historical documents, a high optical character recognition (OCR) word error rate (WER) can render the OCR
text unusable. Since image binarization is often the method used to identify foreground pixels, a body of research seeks
to improve image-wide binarization directly. Instead of relying on any one imperfect binarization technique, our method
incorporates information from multiple simple thresholding binarizations of the same image to improve text output. Using
a new corpus of 19th century newspaper grayscale images for which the text transcription is known, we observe WERs of
13.8% and higher using current binarization techniques and a state-of-the-art OCR engine. Our novel approach combines
the OCR outputs from multiple thresholded images by aligning the text output and producing a lattice of word alternatives
from which a lattice word error rate (LWER) is calculated. Our results show a LWER of 7.6% when aligning two threshold
images and a LWER of 6.8% when aligning five. From the word lattice we commit to one hypothesis by applying the
methods of Lund et al. (2011) achieving an improvement over the original OCR output and a 8.41% WER result on this
data set.
43 citations
TL;DR: This article proposes an approach to identify the layout of a document page by dividing it recursively into nested rectangular areas and uses it as a basis for a document layout model, which is able to control an automatic interpretation mechanism for deriving a high level representation of the contents of a documents.
Abstract: The realization of the paper-free office seems to be difficult that expected. Therefore, good paper-computer interfaces are necessary to transform paper documents into an electronic form, which allows the use of a filing and retrieval system. An electronic document page is an optically scanned and digitized representation of a printed page. Document analysis is the problem of interpreting and labeling the constitutents of the document. Although there are very reliable optical character recognition (OCR) methods, the process could be very inefficient. To prune the search space and to become more efficient, some search supporting methods have to be developed. This article proposes an approach to identify the layout of a document page by dividing it recursively into nested rectangular areas. The procedure is used as a basis for a document layout model, which is able to control an automatic interpretation mechanism for deriving a high level representation of the contents of a document. We have implemented our method in Common Lisp on a Symbolies 3640 Workstation and have run it for a large population of office documents. The results obtained have been very encouraging and have convincingly confirmed the soundness of our approach.
43 citations
TL;DR: A Convolution Neural Network (CNN) based approach to learn strokes, radicals and character features of Chinese characters, and proves that the network structure is superior to LENET-5 in this task.
43 citations