Abstract: Systems and methods for triage of passages of text output from an OCR system by use of trainable models of the accuracy of the OCR system based on attributes of individual characters. The systems and methods according to this invention automatically triage an OCR-output text passage by determining at least one OCR-output character attribute for each OCR-output character, determining an error rate for the OCR-output text passage using a triage model and the determined at least one OCR-output character attribute, and comparing the determined error rate for the OCR-output text passage with an OCR-output text passage threshold error rate to perform an OCR-output text passage triage decision. Triage decision includes for example, sending OCR results directly to an end user without any post-OCR processing, sending the OCR results through a post-OCR inspection and processing stage, sending the original document image to be completely keyed in manually, and a combination thereof.

Abstract: Techniques are provided to classify patterns in isogenous pattern sources. Techniques are provided to determine a computationally inexpensive upperbound on the true score or joint probability of the field label and field features over all field labels. Candidate field labels associated with promising upperbound scores are dynamically queued. True scores are computed for a subset of the candidates fields resulting in reduced computations to determine a field label. Techniques are also provided to determine optimal variables for any system with shared constraints.

Abstract: Systems and methods for triage of passages of text output from an OCR system by use of trainable models of the accuracy of the OCR system based on attributes of individual characters.

Abstract: The method and apparatus enables any user to search for logos in document images stored in a bitmap format. The search efficiently compares bitmap or image data by extracting a series of connected components. These connected components are grouped according to region where each region may be a potential logo. Shape and density parameters of a region are determined and compared to the parameters of the stored logo image. If a region is successfully matched then that region is aligned and scaled to the corresponding stored logo image. Thereafter, a bitwise comparison is then performed between the scaled and aligned region and the logo image. A match score is assigned to each region along with other pertinent information about the region, and is stored in a ranked logo list database. The ranked logo list database represents a list of logos found in the document image.