Extraction of signatures from check background based on a filiformity criterion

DOCUMENT GRAY-SCALE REDUCTION USING A NEURO-FUZZY TECHNIQUE

This paper proposes a new neuro-fuzzy technique suitable for binarization and gray-scale reduction of digital documents. The proposed approach uses both the image gray-scales and additional local spatial features. Both, gray-scales and local feature values feed a Kohonen Self-Organized Feature Map (SOFM) neural network classifier. After training, the neurons of the output competition layer of the SOFM define two bilevel classes. Using the content of these classes, fuzzy membership functions are obtained that are next used by the fuzzy C-means (FCM) algorithm in order to reduce the character-blurring problem. The method is suitable for improving blurring and badly illuminated documents and can be easily modified to accommodate any type of spatial characteristics.

Stroke-model-based character extraction from gray-level document images

Global gray-level thresholding techniques such as Otsu's method, and local gray-level thresholding techniques such as edge-based segmentation or the adaptive thresholding method are powerful in extracting character objects from simple or slowly varying backgrounds. However, they are found to be insufficient when the backgrounds include sharply varying contours or fonts in different sizes. A stroke-model is proposed to depict the local features of character objects as double-edges in a predefined size. This model enables us to detect thin connected components selectively, while ignoring relatively large backgrounds that appear complex. Meanwhile, since the stroke width restriction is fully factored in, the proposed technique can be used to extract characters in predefined font sizes. To process large volumes of documents efficiently, a hybrid method is proposed for character extraction from various backgrounds. Using the measurement of class separability to differentiate images with simple backgrounds from those with complex backgrounds, the hybrid method can process documents with different backgrounds by applying the appropriate methods. Experiments on extracting handwriting from a check image, as well as machine-printed characters from scene images demonstrate the effectiveness of the proposed model.

The segmentation of cursive handwriting: an approach based on off-line recovery of the motor-temporal information

This paper presents a segmentation method that partly mimics the cognitive-behavioral process used by human subjects to recover motor-temporal information from the image of a handwritten word. The approach does not exploit any thinning or skeletonization procedure, but rather a different type of information is manipulated concerning the curvature function of the word contour. In this way, it is possible to detect the parts of the image where the original odometric information is lost or ambiguous (such as, for example, at an intersection of the handwritten lines) and interpret them to finally recover a part of the original temporal information. The algorithm scans the word, following the natural course of the line, and attempts to reproduce the same movement as executed by the writer during the generation of the word. It segments the cursive trace where the contour shows the slow-down of the original movement (corresponding to the maximum curvature points of the curve). At the end of the scanning process, a temporal sequence of motor strokes is obtained which plausibly composed the original intended movement.