Topic
Intelligent word recognition
About: Intelligent word recognition is a(n) research topic. Over the lifetime, 2480 publication(s) have been published within this topic receiving 45813 citation(s).
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TL;DR: “Best of the Web” presents the modified National Institute of Standards and Technology (MNIST) resources, consisting of a collection of handwritten digit images used extensively in optical character recognition and machine learning research.
Abstract: In this issue, “Best of the Web” presents the modified National Institute of Standards and Technology (MNIST) resources, consisting of a collection of handwritten digit images used extensively in optical character recognition and machine learning research.
896 citations
Posted Content•
TL;DR: This work presents a framework for the recognition of natural scene text that does not require any human-labelled data, and performs word recognition on the whole image holistically, departing from the character based recognition systems of the past.
Abstract: In this work we present a framework for the recognition of natural scene text. Our framework does not require any human-labelled data, and performs word recognition on the whole image holistically, departing from the character based recognition systems of the past. The deep neural network models at the centre of this framework are trained solely on data produced by a synthetic text generation engine -- synthetic data that is highly realistic and sufficient to replace real data, giving us infinite amounts of training data. This excess of data exposes new possibilities for word recognition models, and here we consider three models, each one "reading" words in a different way: via 90k-way dictionary encoding, character sequence encoding, and bag-of-N-grams encoding. In the scenarios of language based and completely unconstrained text recognition we greatly improve upon state-of-the-art performance on standard datasets, using our fast, simple machinery and requiring zero data-acquisition costs.
740 citations
01 Jan 1995
TL;DR: This comparison of several learning algorithms for handwritten digits considers not only raw accuracy, but also rejection, training time, recognition time, and memory requirements.
Abstract: COMPARISON OF LEARNINGALGORITHMS FOR HANDWRITTEN DIGITRECOGNITIONY. LeCun, L. Jackel, L. Bottou, A. Brunot, C. Cortes,J. Denker, H. Drucker, I. Guyon, U. M uller,E. Sackinger, P. Simard, and V. VapnikBell Lab oratories, Holmdel, NJ 07733, USAEmail: yann@research.att.comAbstractThis pap er compares the p erformance of several classi er algorithmson a standard database of handwritten digits. We consider not only rawaccuracy, but also rejection, training time, recognition time, and memoryrequirements.1
602 citations
Proceedings Article•
01 Feb 2009TL;DR: It is demonstrated that the performance of the proposed method can be far superior to that of commercial OCR systems, and can benefit from synthetically generated training data obviating the need for expensive data collection and annotation.
Abstract: This paper tackles the problem of recognizing characters in images of natural scenes. In particular, we focus on recognizing characters in situations that would traditionally not be handled well by OCR techniques. We present an annotated database of images containing English and Kannada characters. The database comprises of images of street scenes taken in Bangalore, India using a standard camera. The problem is addressed in an object cateogorization framework based on a bag-of-visual-words representation. We assess the performance of various features based on nearest neighbour and SVM classification. It is demonstrated that the performance of the proposed method, using as few as 15 training images, can be far superior to that of commercial OCR systems. Furthermore, the method can benefit from synthetically generated training data obviating the need for expensive data collection and annotation.
474 citations