Use of colour for hand-filled form analysis and recognition

Colour information in form analysis is currently under utilised. As technology has advanced and computing costs have reduced, the processing of forms in colour has now become practicable. This paper describes a novel colour-based approach to the extraction of filled data from colour form images. Images are first quantised to reduce the colour complexity and data is extracted by examining the colour characteristics of the images. The improved performance of the proposed method has been verified by comparing the processing time, recognition rate, extraction precision and recall rate to that of an equivalent black and white system

Quality of Data Entry Using Single Entry, Double Entry and Automated Forms Processing–An Example Based on a Study of Patient-Reported Outcomes

Background: The clinical and scientific usage of patient-reported outcome measures is increasing in the health services. Often paper forms are used. Manual double entry of data is defined as the definitive gold standard for transferring data to an electronic format, but the process is laborious. Automated forms processing may be an alternative, but further validation is warranted. Methods: 200 patients were randomly selected from a cohort of 5777 patients who had previously answered two different questionnaires. The questionnaires were scanned using an automated forms processing technique, as well as processed by single and double manual data entry, using the EpiData Entry data entry program. The main outcome measure was the proportion of correctly entered numbers at question, form and study level. Results: Manual double-key data entry (error proportion per 1000 fields = 0.046 (95 % CI: 0.001–0.258)) performed better than single-key data entry (error proportion per 1000 fields = 0.370 (95 % CI: 0.160–0.729), (p = 0.020)). There was no statistical difference between Optical Mark Recognition (error proportion per 1000 fields = 0.046 (95 % CI: 0.001–0.258)) and double-key data entry (p = 1.000). With the Intelligent Character Recognition method, there was no statistical difference compared to single-key data entry (error proportion per 1000 fields = 6.734 (95 % CI: 0.817–24.113), (p = 0.656)), as well as double-key data entry (error proportion per 1000 fields = 3.367 (95 % CI: 0.085–18.616)), (p = 0.319))

Form processing with the Hough transform

A form document processing system based on the Hough transform (HT) is developed. It performs form identification and form registration. For form identification, HT is applied off-line to master forms to calculate form features and build-up the feature database, and it is performed on-line for the input (scanned) forms to extract features to identify the form type based on feature matching. The derived features are rotation, translation and scale invariant. The proposed form description is compact, thereby allows for fast identification. The registration is feature/knowledge based. Two methods for control points detection are discussed; one implements template matching for finding frame corners. The second approach is based on detection of line crossings via the analysis of the parameter space of the HT. Detected control points are used to calculate parameters of geometrical transform and perform coordinates translation. Linear conformal and projective transforms are tested. The system is featured by fast and reliable type identification, and the moderate preprocessing time, which is attained by proper design of the Hough space

Form Analysis by Neural Classification of Cells

The original publication is available at www.springerlink.com/www.springerlink.comOur aim in this paper is to present a methodology for linearly combining multi neural classifier for cell analysis of forms. Features used for the classification are relative to the text orientation and to its character morphology. Eight classes are extracted among numeric, alphabetic, vertical, horizontal, capitals, etc. Classifiers are multi-layered perceptrons considering firstly global features and refining the classification at each step by looking for more precise features. The recognition rate of the classifiers for 3. 500 cells issued from 19 forms is about 91 %