Publication

Handwriting Recognition

1995
Conference Papers

Abstract

Recognition of handwritten text by computer can significantly reduce the cost of manual key entry in applications such as bank check processing, postal mail routing, census and poll form capture, medical documents, and many others. In this review, I will describe the structure and performance of a handwriting recognition system. This system has demonstrated competitive performance in an evaluation by the {US} {C}ensus {B}ureau and is currently being used in the 1995 {US} {T}est {C}ensus for transcribing handwritten names, telephone numbers, and other information into {ASCII} text. The system operates on unsegmented handwritten input strings (i.e., there are no preprinted boxes into which respondents write characters). Like more general machine vision tasks, handwriting recognition forces us to address questions of feature extraction, figure/ground, image segmentation, shape variation, and the integration of top-down knowledge.

Official source

https://infoscience.epfl.ch/entities/publication/bdb6ed87-70bc-4bfc-9709-9c11e7847580

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

1995
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/bdb6ed87-70bc-4bfc-9709-9c11e7847580

About this result

Related concepts (32)

Handwriting recognition

Handwriting recognition (HWR), also known as handwritten text recognition (HTR), is the ability of a computer to receive and interpret intelligible handwritten input from sources such as paper documents, photographs, touch-screens and other devices. The image of the written text may be sensed "off line" from a piece of paper by optical scanning (optical character recognition) or intelligent word recognition. Alternatively, the movements of the pen tip may be sensed "on line", for example by a pen-based computer screen surface, a generally easier task as there are more clues available.

Optical character recognition

Optical character recognition or optical character reader (OCR) is the electronic or mechanical conversion of s of typed, handwritten or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a scene photo (for example the text on signs and billboards in a landscape photo) or from subtitle text superimposed on an image (for example: from a television broadcast).

Pattern recognition

Pattern recognition is the automated recognition of patterns and regularities in data. While similar, pattern recognition (PR) is not to be confused with pattern machines (PM) which may possess (PR) capabilities but their primary function is to distinguish and create emergent pattern. PR has applications in statistical data analysis, signal processing, , information retrieval, bioinformatics, data compression, computer graphics and machine learning.

Related publications (32)

Handwriting Recognition

Chat with Graph Search

Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts

Data-Driven Robust Controller Synthesis Framework using Non-Parametric Integral Quadratic Constraints

Unsupervised Pattern Discovery for Mobile Network Tuning Optimization