Stanley Janet

NIST Form-Based Handprint Recognition System

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Federal Register document image database

A new, fully-automated process has been developed at NIST to derive ground truth for document images. The method involves matching optical character recognition (OCR) results from a page with typesetting files for an entire book. Public domain software used to derive the ground truth is provided in the form of Perl scripts and C source code, and includes new, more efficient string alignment technology and a word- level scoring package. With this ground truthing technology, it is now feasible to produce much larger data sets, at much lower cost, than was ever possible with previous labor- intensive, manual data collection projects. Using this method, NIST has produced a new document image database for evaluating Document Analysis and Recognition technologies and Information Retrieval systems. The database produced contains scanned images, SGML-tagged ground truth text, commercial OCR results, and image quality assessment results for pages published in the 1994 Federal Register. These data files are useful in a wide variety of experiments and research. There were roughly 250 issues, comprised of nearly 69,000 pages, published in the Federal Register in 1994. This volume of the database contains the pages of 20 books published in January of that year. In all, there are 4711 page images provided, with 4519 of them having corresponding ground truth. This volume is distributed on two ISO-9660 CD- ROMs. Future volumes may be released, depending on the level of interest.

Off-line handwriting recognition from forms

A public domain optical character recognition (OCR) system has been developed by the National Institute of Standards and Technology (NIST) to provide a baseline of performance on off-line handwriting recognition from forms. The systems source code, training data, and performance assessment tools are all publicly available. The system recognizes the handprint written on handwriting sample forms as distributed on the CD-ROM, NIST Special Database 19. The public domain package contains a number of significant contributions to OCR technology, including an optimized probabilistic neural network classifier that operates a factor of 20 times faster than traditional software implementations of this algorithm. The modular design of the software makes it useful for training and testing set validation, multiple system voting schemes, and component evaluation and comparison. As an example, the OCR results from two versions of the recognition system are presented and analyzed.

Design of a handprint recognition system

A public domain optical character recognition (OCR) system has been developed by the National Institute of Standards and Technology (NIST). This standard reference form-based handprint recognition system is designed to provide a baseline of performance on an open application. The systems source code, training data, performance assessment tools, and type of forms processed are all publicly available. The system is modular, allowing for system component testing and comparisons, and it can be used to validate training and testing sets in an end-to-end application. The systems source code is written in C and will run on virtually any UNIX-based computer. The presented functional components of the system are divided into three levels of processing: (1) form-level processing includes the tasks of form registration and form removal; (2) field-level processing includes the tasks of field isolation, line trajectory reconstruction, and field segmentation; and (3) character-level processing includes character normalization, feature extraction, character classification, and dictionary-based postprocessing. The system contains a number of significant contributions to OCR technology, including an optimized probabilistic neural network (PNN) classifier that operates a factor of 20 times faster than traditional software implementations of the algorithm. Provided in the system are a host of data structures and low-level utilities for computing spatial histograms, least-squares fitting, spatial zooming, connected components, Karhunen Loe` ve feature extraction, optimized PNN classification, and dynamic string alignment. Any portion of this standard reference OCR system can be used in commercial products without restrictions.

Massively parallel implementation of character recognition systems

A massively parallel character recognition system has been implemented. The system is designed to study the feasibility of the recognition of handprinted text in a loosely constrained environment. The NIST handprint database, NIST Special Database 1, is used to provide test data for the recognition system. The system consists of eight functional components. The loading of the image into the system and storing the recognition results from the system are I/O components. In between are components responsible for image processing and recognition. The first image processing component is responsible for image correction for scale and rotation, data field isolation, and character data location within each field; the second performs character segmentation; and the third does character normalization. Three recognition components are responsible for feature extraction and character reconstruction, neural network-based character recognition, and low-confidence classification rejection. The image processing to load and isolate 34 fields on a scientific workstation takes 900 seconds. The same processing takes only 11 seconds using a massively parallel array processor. The image processing components, including the time to load the image data, use 94 of the system time. The segmentation time is 15 ms/character and segmentation accuracy is 89 for handprinted digits and alphas. Character recognition accuracy for medium quality machine print is 99.8. On handprinted digits, the recognition accuracy is 96 and recognition speeds of 10,100 characters/second can be realized. The limiting factor in the recognition portion of the system is feature extraction, which occurs at 806 characters/second. Through the use of a massively parallel machine and neural recognition algorithms, significant improvements in both accuracy and speed have been achieved, making this technology effective as a replacement for key data entry in existing data capture systems.

Public domain optical character recognition

A public domain document processing system has been developed by the National Institute of Standards and Technology (NIST). The system is a standard reference form-based handprint recognition system for evaluating optical character recognition (OCR), and it is intended to provide a baseline of performance on an open application. The systems source code, training data, performance assessment tools, and type of forms processed are all publicly available. The system recognizes the handprint entered on handwriting sample forms like the ones distributed with NIST Special Database 1. From these forms, the system reads hand-printed numeric fields, upper and lowercase alphabetic fields, and unconstrained text paragraphs comprised of words from a limited-size dictionary. The modular design of the system makes it useful for component evaluation and comparison, training and testing set validation, and multiple system voting schemes. The system contains a number of significant contributions to OCR technology, including an optimized probabilistic neural network (PNN) classifier that operates a factor of 20 times faster than traditional software implementations of the algorithm. The source code for the recognition system is written in C and is organized into 11 libraries. In all, there are approximately 19,000 lines of code supporting more than 550 subroutines. Source code is provided for form registration, form removal, field isolation, field segmentation, character normalization, feature extraction, character classification, and dictionary-based postprocessing. The recognition system has been successfully compiled and tested on a host of UNIX workstations. This paper gives an overview of the recognition systems software architecture, including descriptions of the various system components along with timing and accuracy statistics.