Optical character recognition in real environments using neural networks and k-nearest neighbor

Matei, Oliviu; Pop, Petrică C.; Vălean, Honoriu

doi:10.1007/s10489-013-0456-2

Cited by 23 publications

(9 citation statements)

References 11 publications

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“…HIM=0.1 (green squares) guide the ants to use the available edge (C E) in 10% of the cases. If the edges (B C) and (C E) are not used, then the ants move based on their transition rule only to the available vertices having zero values in HIM (light blue squares) could observe topological properties, unavailable to artificial agents; other similar ideas are presented in [79][80][81].…”

Section: Future Outlookmentioning

confidence: 99%

Interactive machine learning: experimental evidence for the human in the algorithmic loop

et al. 2018

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Recent advances in automatic machine learning (aML) allow solving problems without any human intervention. However, sometimes a human-in-the-loop can be beneficial in solving computationally hard problems. In this paper we provide new experimental insights on how we can improve computational intelligence by complementing it with human intelligence in an interactive machine learning approach (iML). For this purpose, we used the Ant Colony Optimization (ACO) framework, because this fosters multi-agent approaches with human agents in the loop. We propose unification between the human intelligence and interaction skills and the computational power of an artificial system. The ACO framework is used on a case study solving the Traveling Salesman Problem, because of its many practical implications, e.g. in the medical domain. We used ACO due to the fact that it is one of the best algorithms used in many applied intelligence problems. For the evaluation we used gamification, i.e. we implemented a snake-like game called Traveling Snakesman with the MAX-MIN Ant System (MMAS) in the background. We extended the MMAS-Algorithm in a way, that the human can directly interact and influence the ants. This is done by "traveling" with the snake across the graph. Each time the human travels over an ant, the current pheromone value of the edge is multiplied by 5. This manipulation has an impact on the ant's behavior (the probability that this edge is taken by the ant increases). The results show that the humans performing one tour through the graphs have a significant impact on the shortest path found by the MMAS. Consequently, our experiment demonstrates that in our case human intelligence can positively influence machine intelligence. To the best of our knowledge this is the first study of this kind.

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Section: Future Outlookmentioning

confidence: 99%

Interactive machine learning: experimental evidence for the human in the algorithmic loop

et al. 2018

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“…Thus ordinarily various classifiers are consolidated together to solve a given classification problem. Matei, Oliviu, Petrica C. Pop, and H. Vălean [26] by utilizing neural networks and k-Nearest Neighbor, proposed Optical character recognition in real environments such as electricitymeters and gas-meters.…”

Section: Combination Classifiermentioning

confidence: 99%

A Detailed Analysis of Optical Character Recognition Technology

Hamad

Kaya

2016

International Journal of Applied Mathematics, Electronics and Computers

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Abstract:In many different fields, there is a high demand for storing information to a computer storage disk from the data available in printed or handwritten documents or images to later re-utilize this information by means of computers. One simple way to store information to a computer system from these printed documents could be first to scan the documents and then store them as image files. But to re-utilize this information, it would very difficult to read or query text or other information from these image files. Therefore a technique to automatically retrieve and store information, in particular text, from image files is needed. Optical character recognition is an active research area that attempts to develop a computer system with the ability to extract and process text from images automatically. The objective of OCR is to achieve modification or conversion of any form of text or text-containing documents such as handwritten text, printed or scanned text images, into an editable digital format for deeper and further processing. Therefore, OCR enables a machine to automatically recognize text in such documents. Some major challenges need to be recognized and handled in order to achieve a successful automation. The font characteristics of the characters in paper documents and quality of images are only some of the recent challenges. Due to these challenges, characters sometimes may not be recognized correctly by computer system. In this paper we investigate OCR in four different ways. First we give a detailed overview of the challenges that might emerge in OCR stages. Second, we review the general phases of an OCR system such as pre-processing, segmentation, normalization, feature extraction, classification and post-processing. Then, we highlight developments and main applications and uses of OCR and finally, a brief OCR history are discussed. Therefore, this discussion provides a very comprehensive review of the state-of-the-art of the field.

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“…Then we choose the minimum number of samples in the variational row to be blocked by zero to fulfil the equality condition in Eq. (16). If for instance, the calculation result of the first parenthesis is greater than zero, we set the magnitudes in the variational row equal to zero, starting with the highest value sample.…”

Section: Recognitionmentioning

confidence: 99%

Nonlinear filtering for character recognition in low quality document images

Diaz-Escobar

Kober

2014

SPIE Proceedings

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Optical character recognition in scanned printed documents is a well-studied task, where the captured conditions like sheet position, illumination, contrast and resolution are controlled. Nowadays, it is more practical to use mobile devices for document capture than a scanner. So as a consequence, the quality of document images is often poor owing to presence of geometric distortions, nonhomogeneous illumination, low resolution, etc. In this work we propose to use multiple adaptive nonlinear composite filters for detection and classification of characters. Computer simulation results obtained with the proposed system are presented and discussed.

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Optical character recognition in real environments using neural networks and k-nearest neighbor

Cited by 23 publications

References 11 publications

Interactive machine learning: experimental evidence for the human in the algorithmic loop

Interactive machine learning: experimental evidence for the human in the algorithmic loop

A Detailed Analysis of Optical Character Recognition Technology

Nonlinear filtering for character recognition in low quality document images

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