Optical character recognition

About: Optical character recognition is a research topic. Over the lifetime, 7342 publications have been published within this topic receiving 158193 citations. The topic is also known as: OCR & optical character reader.

Learning on the Fly: Font-Free Approaches to Difficult OCR Problems

Abstract: Despite ubiquitous claims that optical character recognition (OCR) is a "solved problem,'' many categories of documents continue to break modern OCR software such as documents with moderate degradation or unusual fonts. Many approaches rely on pre-computed or stored character models, but these are vulnerable to cases when the font of a particular document was not part of the training set, or when there is so much noise in a document that the font model becomes weak. To address these difficult cases, we present a form of iterative contextual modeling that learns character models directly from the document it is trying to recognize. We use these learned models both to segment the characters and to recognize them in an incremental, iterative process. We present results comparable to those of a commercial OCR system on a subset of characters from a difficult test document.

Script recognition in images with complex backgrounds

Abstract: The extraction of textual information from images and videos is an important task for automatic content-based indexing and retrieval purposes. To extract text from images or videos coming from unknown international sources, it is necessary to know the script beforehand in order to employ suitable text segmentation and optical character recognition (OCR) methods. In this paper, we present an approach for discriminating between Latin and Ideographic script. The proposed approach proceeds as follows: first, the text present in an image is localized. Then, a set of low-level features is extracted from the localized text image. Finally, based on the extracted features, the decision about the type of the script is made using a k-nearest neighbour classifier. Initial experimental results for a set of images containing text of different scripts demonstrate the good performance of the proposed solution

Word level Script Identification from Bangla and Devanagri Handwritten Texts mixed with Roman Script

Abstract: India is a multi-lingual country where Roman script is often used alongside different Indic scripts in a text document. To develop a script specific handwritten Optical Character Recognition (OCR) system, it is therefore necessary to identify the scripts of handwritten text correctly. In this paper, we present a system, which automatically separates the scripts of handwritten words from a document, written in Bangla or Devanagri mixed with Roman scripts. In this script separation technique, we first, extract the text lines and words from document pages using a script independent Neighboring Component Analysis technique (1). Then we have designed a Multi Layer Perceptron (MLP) based classifier for script separation, trained with 8 different word- level holistic features. Two equal sized datasets, one with Bangla and Roman scripts and the other with Devanagri and Roman scripts, are prepared for the system evaluation. On respective independent text samples, word-level script identification accuracies of 99.29% and 98.43% are achieved.

Document Scanning and Data Derivation Architecture.

Abstract: Proprietary suite of underlying document image analysis capabilities, including a novel forms enhancement, segmentation and modeling component, forms recognition and optical character recognition. Future version of the system will include form reasoning to detect and classify fields on forms with varying layout. Product provides acquisition, modeling, recognition and processing components, and has the ability to verify recognized data on the image with a line by line comparison. The key enabling technologies center around the recognition and processing of the scanned forms. The system learns the positions of lines and the location of text on the pre-printed form, and associates various regions of the form with specific required fields in the electronic version. Once the form is recognized, the preprinted material is removed and individual regions are passed to an optical character recognition component. The current proprietary OCR engine is trained with a variety of Roman text fonts and has a back end dictionary that can be customized to account for the fact that the system knows which field it is recognizing. The engine performs segmentation to obtain isolated characters and computes a structure based feature vector. The characters are normalized and classified using a cluster centric classifier, which responds well to variations in the symbols contour. An efficient dictionary lookup scheme provides exact and edit distance lookup using a TRIE structure. An edit distance is computed and a collection of near misses can be output in a lattice to enhance the final recognition result. The current classification rate can exceed 99% with context. The ultimate goal of this system is to enable the processing of all tax forms including forms with handwritten material.