Publication

Recognition of Handprinted Digits using Optimal Bounded Error Matching

1993
Conference Papers

Abstract

This paper describes a system for recognizing hand-printed digits using optimal bounded error matching. Bounded error matching is already in common use in general-purpose 2D and 3D visual object recognition and can cope with clutter, occlusions, and noise, important issues also in OCR. The results presented demonstrate that the same techniques achieve high recognition rates (up to 99.2%) on a real-world hand- printed digit recognition task (the {NIST} database of hand-printed census forms and the {CEDAR} database of digits extracted from the {U.S.} mail ZIP codes). As part of the system, a post-processing step for k-nearest neighbor clasifiers based on decision trees is described that can be used (in place of the usual heuristic methods) for setting thresholds and that improves recognition rates significantly.

Official source

https://infoscience.epfl.ch/entities/publication/0abe4d2e-53e2-4e30-9c1c-481c0f18c5c2

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.

1993
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/0abe4d2e-53e2-4e30-9c1c-481c0f18c5c2

About this result

Related concepts (31)

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).

Speech recognition

Speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers. It is also known as automatic speech recognition (ASR), computer speech recognition or speech to text (STT). It incorporates knowledge and research in the computer science, linguistics and computer engineering fields. The reverse process is speech synthesis.

Facial recognition system

A facial recognition system is a technology potentially capable of matching a human face from a or a video frame against a database of faces. Such a system is typically employed to authenticate users through ID verification services, and works by pinpointing and measuring facial features from a given image. Development began on similar systems in the 1960s, beginning as a form of computer application. Since their inception, facial recognition systems have seen wider uses in recent times on smartphones and in other forms of technology, such as robotics.

Related publications (25)

Recognition of Handprinted Digits using Optimal Bounded Error Matching

Chat with Graph Search

Toward Semantic Gaze Target Detection

Score Calibration in Face Recognition

Feature Mapping of Multiple Beamformed Sources for Robust Overlapping Speech Recognition Using a Microphone Array