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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.


Papers
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Proceedings ArticleDOI
18 Aug 1997
TL;DR: This work investigates techniques to combine multiple representations of a handwritten digit to increase classification accuracy without significantly increasing system complexity or recognition time and implements and compares voting, mixture of experts, stacking and cascading.
Abstract: We investigate techniques to combine multiple representations of a handwritten digit to increase classification accuracy without significantly increasing system complexity or recognition time. We compare multiexpert and multistage combination techniques and discuss in detail in a comparative manner methods for combining multiple learners: voting, mixture of experts, stacking, boosting and cascading. In pen based handwritten character recognition, the input is the dynamic movement of the pentip over the pressure sensitive tablet. There is also the image formed as a result of this movement. On a real world database, we notice that the two multi layer perceptron (MLP) neural network based classifiers using these representations separately make errors on different patterns, implying that a suitable combination of the two would lead to higher accuracy. Thus we implement and compare voting, mixture of experts, stacking and cascading. Combined classifiers have an error percentage less than individual ones. The final combined system of two MLPs has less complexity and memory requirement than a single k nearest neighbor using one of the representations.

122 citations

Journal ArticleDOI
TL;DR: The memory requirements are uniquely designed to be extremely low, which enables usage of smaller FPGAs, and the resulting hardware is suitable for applications where cost, compactness, and efficiency are system design constraints.
Abstract: In this paper, a video processing methodology for a field-programmable gate array (FPGA)-based license plate recognition (LPR) system is researched. The raster scan video is used as an input with low memory utilization. During the design, Gabor filter, threshold, and connected component labeling (CCL) algorithms are used to obtain license plate region. This region is segmented into disjoint characters for the character recognition phase, where the self-organizing map (SOM) neural network is used to identify the characters. The system is portable and relatively faster than computer-based recognition systems. The robustness of the system has been tested with a large database acquired from parking lots and a highway. The memory requirements are uniquely designed to be extremely low, which enables usage of smaller FPGAs. The resulting hardware is suitable for applications where cost, compactness, and efficiency are system design constraints.

122 citations

Journal ArticleDOI
TL;DR: This article describes the application of neural and fuzzy methods to three problems: recognition of handwritten words; recognition of numeric fields; and location of handwritten street numbers in address images.
Abstract: Handwriting recognition requires tools and techniques that recognize complex character patterns and represent imprecise, common-sense knowledge about the general appearance of characters, words and phrases. Neural networks and fuzzy logic are complementary tools for solving such problems. Neural networks, which are highly nonlinear and highly interconnected for processing imprecise information, can finely approximate complicated decision boundaries. Fuzzy set methods can represent degrees of truth or belonging. Fuzzy logic encodes imprecise knowledge and naturally maintains multiple hypotheses that result from the uncertainty and vagueness inherent in real problems. By combining the complementary strengths of neural and fuzzy approaches into a hybrid system, we can attain an increased recognition capability for solving handwriting recognition problems. This article describes the application of neural and fuzzy methods to three problems: recognition of handwritten words; recognition of numeric fields; and location of handwritten street numbers in address images.

121 citations

Journal ArticleDOI
TL;DR: An automatic off-line character recognition system for handwritten cursive Arabic characters is presented and proved to be powerful in tolerance to variable writing, speed, and recognition rate.
Abstract: An automatic off-line character recognition system for handwritten cursive Arabic characters is presented. A robust noise-independent algorithm is developed that yields skeletons that reflect the structural relationships of the character components. The character skeleton is converted to a tree structure suitable for recognition. A set of fuzzy constrained character graph models (FCCGM's), which tolerate large variability in writing, is designed. These models are graphs, with fuzzily labeled arcs used as prototypes for the characters. A set of rules is applied in sequence to match a character tree to an FCCGM. Arabic handwritings of four writers were used in the learning and testing stages. The system proved to be powerful in tolerance to variable writing, speed, and recognition rate. >

121 citations

Journal ArticleDOI
01 Mar 2014
TL;DR: The Urdu, Pushto, and Sindhi languages are discussed, with the emphasis being on the Nasta'liq and Naskh scripts, with an emphasis on the preprocessing, segmentation, feature extraction, classification, and recognition in OCR.
Abstract: We survey the optical character recognition (OCR) literature with reference to the Urdu-like cursive scripts. In particular, the Urdu, Pushto, and Sindhi languages are discussed, with the emphasis being on the Nasta'liq and Naskh scripts. Before detaining the OCR works, the peculiarities of the Urdu-like scripts are outlined, which are followed by the presentation of the available text image databases. For the sake of clarity, the various attempts are grouped into three parts, namely: (a) printed, (b) handwritten, and (c) online character recognition. Within each part, the works are analyzed par rapport a typical OCR pipeline with an emphasis on the preprocessing, segmentation, feature extraction, classification, and recognition. HighlightsA literature review of the Nasta'liq and Naskh cursive script OCR.The peculiarities and challenges are described a priori.Printed, handwritten and online OCR efforts are being explored.Analyses based on the stages of a typical OCR pipeline.

121 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023186
2022425
2021333
2020448
2019430
2018357