Character Recognition TRIZ Cases

OCR-based extraction system reconstructs table hierarchy from greyscale images, eliminating manual selection errors and preserving data integrity.

System applies preliminary quality assessment on text regions extracted from multi-zoom images to filter low-resolution areas before processing.

A text localization method removes lines and graphics from images using geometric manipulations and the Hough transform.

A character string reading device selects output candidates based on matching formats to reduce operator fatigue.

Segmented binarization resolves luminance contradictions by applying region-specific thresholds, ensuring high character recognition accuracy.

Global text angle estimation refines bounding box geometry to resolve local estimation inaccuracies for angled text.

Fuzzy regular expressions process noisy OCR data to resolve extraction accuracy errors and improve content identification reliability.

A classification model determines character noise using size, distance, and confidence levels.

An information processing apparatus determines document types from titles to extract field values using definition information.

Segmenting paper documents into portions reduces processing time while maintaining text recognition accuracy through automated motion-based alignment.

An image processing device expands and unites object regions to identify character groups for targeted recognition.

A reliability calculation unit evaluates character recognition confidence to select specific output destinations for processing results.

Feature fusion in a backbone network generates separate heat maps for character and text line segmentation, resolving touching character detection issues.

An image processing apparatus calculates similarity degrees between character strings and candidate groups to enhance recognition precision.

Mobile camera detects text in video streams, sending compressed subregions to remote servers to bypass bulky stylus input and limited device resources.

A table recognition system extracts merging features to determine row merging directions and adjusts candidate results for accurate structure.

Information processing apparatus detects image types during scanning to automate re-designation workflows.

Segmented classifiers distinguish visually similar objects while reducing false positives.