Yaakov Navon

A Generic Form Processing Approach for Large Variant Templates

In today’s world, form processing systems must be able to recognize mutant forms that appear to be based on differing templates but are actually only a variation of the original. A single definition of a representative template actually covers large varieties of the same logical templates. We developed a method and system, similar to the human visual system, which differentiates between templates via features such as logos, dominant words, and geometrical shapes, while ignoring minor details and variations. When the system finds an appropriate template, it then decodes the content of the form. Our approach has been applied in several scenarios with encouraging results.

Layer-based binarization for textual images

We developed a binarization approach to handle a large variety of images, from scanned flatbed images to images acquired by mobile phone cameras. The binarization is targeted at creating layers of binary images for processing by OCR engines. The layers are classified spatially and by intensity and color. First textual pixels are classified by a text operator. The text kernel is then segmented by intensity/color levels and layout analysis techniques to create regions of similar text. Finally, adaptive binarization is applied to each region to obtain superior binary images. Our experimental results show the advantages of our method over local binarization methods.

Enhanced Active Contour Method for Locating Text

Document analysis of images photographed by camera-equipped mobile phones is a growing challenge. These photos are often poor-quality compound images, composed of various objects and text, this makes automatic analysis complicated, thereby limiting the usefulness of the images. Existing image processing techniques do not manage to clearly decipher the text in such pictures. We developed a method for precisely locating text in complex scene images that can then be further processed by OCR systems. A text kernel operator roughly locates the text in an image. This information then serves as an initialization for the active contour method. This technique enhances the convergence process of the active contour and significantly speeds up the overall process. Moreover, our initialization settings enable systems to easily distinguish between the inside and outside parts of contours. Our experimental results show a significant improvement in the ability to locate and preprocess text.

Detecting highlights and notes on printed text

The extensive use of digital cameras in creating an electronic database of document images, leads to a major problem in managing the vast amount of information. Many images include user-added marks, such as highlights and handwritten notes, which can be used in content based image retrieval. This paper presents a novel method for determining the existence of user-added marks in a given image and classification of existing marks. Two different approaches are integrated for the detection of marks: one based on color-spaces and the other on anomaly detection in text-dominant images. The proposed method achieved very successful results.

Image Processing Based Approach for Retrieving Data from a Seismic Section in Bitmap Format

A new method for retrieving seismic data from a seismic section provided in a bitmap format is described. The method is based on image processing techniques and includes creating a grey level image of a seismic section, processing the grey level image (by integration, filtering, etc.) and then reconstructing digitized values of individual seismic traces_ from the resulting image, thus ending with the data in standard SEG-Y format