Post processing is the most conventional approach for correcting errors that are caused by Optical Character Recognition(OCR) systems. Two steps are usually taken to correct OCR errors: detection and corrections. For the first task, supervised machin
e learning methods have shown state-of-the-art performances. Previously proposed approaches have focused most prominently on combining lexical, contextual and statistical features for detecting errors. In this study, we report a novel system to error detection which is based merely on the n-gram counts of a candidate token. In addition to being simple and computationally less expensive, our proposed system beats previous systems reported in the ICDAR2019 competition on OCR-error detection with notable margins. We achieved state-of-the-art F1-scores for eight out of the ten involved European languages. The maximum improvement is for Spanish which improved from 0.69 to 0.90, and the minimum for Polish from 0.82 to 0.84.
Historical corpora are known to contain errors introduced by OCR (optical character recognition) methods used in the digitization process, often said to be degrading the performance of NLP systems. Correcting these errors manually is a time-consuming
process and a great part of the automatic approaches have been relying on rules or supervised machine learning. We build on previous work on fully automatic unsupervised extraction of parallel data to train a character-based sequence-to-sequence NMT (neural machine translation) model to conduct OCR error correction designed for English, and adapt it to Finnish by proposing solutions that take the rich morphology of the language into account. Our new method shows increased performance while remaining fully unsupervised, with the added benefit of spelling normalisation. The source code and models are available on GitHub and Zenodo.
Designing Computerized Systems which posses reading and hearing
faculties is an active research area for more than four decades. Many
methods and algorithms have been suggested by researches for this
purpose as part of pattern recognition research
. Recently, more
research work has been devoted to the holist approach the
recognition system recognizes a complete word as one object without
going through the long and erroneous character segmentation
process. In this paper, a convolutional neural network has been
designed to recognize the popular Arabic names holistically. SUSt
ARG names data set has been used to test the network performance
(collected and compiled by pattern recognition research in Sudan
University of Science and Technology-SUSt). Selecting an appropriate
deep learning toolbox, after five stages of training, the network was
able to recognize all the names and 100%
The Automatic recognition System to vehicles through its number is an important topic, because of its important uses, such as security applications by monitoring the entrances of a important institutions, monitor the vehicles on the road, detection o
f stolen cars, and even that could be useful in statistical studies, where we can study the traffic congestion in an area. In this work we offer an overview of the Automatic Number Plate Recognition System (ANPR) through to identify the license plate number, and also recognize the color of car.
The focus of this research on the stage of converting the numbers into a picture of a car plate to actual figures, to improve the performance of all system, where many of errors that occur at this stage.
In this search was used the algorithm of Principle component
analysis (PCA) to identify the numbers plate inside the picture.
and its integration with optical character Recognition
algorithm(OCR) which usually used for recognition , to minimize
errors in recognition numbers and thus improve the performance of the automatic number plate system.and also we add color car recognize(which another important parameter of car) , this helps after return to data base detect stolen vehicles and improve the reliability of system
