Handwritten digit recognition: benchmarking of state-of-the-art techniques

In this paper, a system for the reading of totally unconstrained handwritten text is presented. The kernel of the system is a hidden Markov model (HMM) for handwriting recognition. The HMM is enhanced by a statistical language model. Thus linguistic knowledge beyond the lexicon level is incorporated in the recognition process. Another novel feature of the system is that the HMM is applied in such a way that the difficult problem of segmenting a line of text into individual words is avoided. A number of experiments with various language models and large vocabularies have been conducted. The language models used in the system were also analytically compared based on their perplexity.

Using a statistical language model to improve the performance of an HMM-based cursive handwriting recognition systems

A method is provided for modifying an image. The method comprises displaying an image, the image comprising a portion of an object; and determining if an edge of the object is in a location within the portion. The method further comprises detecting movement, in a member direction, of an operating member with respect to the edge. The method still further comprises moving, if the edge is not in the location, the object in an object direction corresponding to the detected movement; and modifying, if the edge is in the location, the image in response to the detected movement, the modified image comprising the edge in the location.

Information processing apparatus, information processing method and program

The holistic paradigm in handwritten word recognition treats the word as a single, indivisible entity and attempts to recognize words from their overall shape, as opposed to their character contents. In this survey, we have attempted to take a fresh look at the potential role of the holistic paradigm in handwritten word recognition. The survey begins with an overview of studies of reading which provide evidence for the existence of a parallel holistic reading process,in both developing and skilled readers. In what we believe is a fresh perspective on handwriting recognition, approaches to recognition are characterized as forming a continuous spectrum based on the visual complexity of the unit of recognition employed and an attempt is made to interpret well-known paradigms of word recognition in this framework. An overview of features, methodologies, representations, and matching techniques employed by holistic approaches is presented.

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The role of holistic paradigms in handwritten word recognition

In various embodiments, methods, systems, and computer program products for processing digital images captured by a mobile device are disclosed. Myriad features enable and/or facilitate processing of such digital images using a mobile device that would otherwise be technically impossible or impractical, and furthermore address unique challenges presented by images captured using a camera rather than a traditional flat-bed scanner, paper-feed scanner or multifunction peripheral.

Systems and methods for mobile image capture and processing

In a combined holistic and analytic recognition system, the holistic recognition module will recognize an input word or phrase image by matching an input string of character features for the whole word or phrase against a string of prototype features for a plurality of reference words in a lexicon. This will yield a holistic answer list of recognized word or phrase candidates for the input word or phrase along with a confidence value for each answer on the list. At the same time based on each answer in the answer list, the holistic recognition modules will generate a list of character features and segment the character features into sets of each character in an answer. The analytical recognition module uses segmentation hypotheses from the segmented character feature sets to cut the image of the input string of characters into individual character images. A plurality of character images for the various segmentation hypotheses will be recognized to produce an analytical answer list having a plurality of word or phrase answers for the input word or phrase. Each analytic word answer will have a confidence value based on the combined confidence of recognizing each character. The holistic answer list and the analytic answer list will be examined to find the best answer from the two lists as the recognition of the input handwritten text.

Holistic-analytical recognition of handwritten text

Check amount recognition is one of the most promising commercial applications of handwriting recognition This paper is devoted to the description of the check reading system developed to recognize amounts on American personal checks Special attention is paid to a reliable procedure developed to reject doubtful answers For this purpose the legal (worded) amount on a personal check is recognized along with the courtesy (digit) amount For both courtesy and legal amount fields, a brief description of all recognition stages beginning with field extraction and ending with the recognition itself are presented We also present the explanation of problems existing at each stage and their possible solutions The numeral recognizer used to read the amounts written in figures is described This recognizer is based on the procedure of matching input subgraphs to graphs of symbol prototypes Main principles of the handwriting recognizer used to read amounts written in words are explained The recognizer is based on the idea of describing the handwriting with the most stable handwriting elements The concept of the optimal confidence level of the recognition answer is introduced It is shown that the conditional probability of the answer correctness is an optimal confidence level function The algorithms of the optimal confidence level estimation for some special cases are described The sophisticated algorithm of cross validation between legal and courtesy amount recognition results based on the optimal confidence level approach is proposed Experimental results on real checks are presented The recognition rate at 1% error rate is 67% The recognition rate without reject is 85% Significant improvement is achieved due to legal amount processing in spite of a relatively low recognition rate for this field

Check Amount Recognition Based on the Cross Validation of Courtesy and Legal Amount Fields

Methods and systems are disclosed to aid in the detection of areas of interest in an image. Multiple image recognition processes analyze the image and identify areas of interest. The identified areas of interest are compared to determine confidence values for each identified area of interest using a voting process. The confidence values may be used in determining areas of increased interest which are highlighted on the image. In embodiments, identified areas of interest meeting a certain threshold requirement are selected as areas of increased interest. In other embodiments, new areas of increased interest are created by combining areas of interest. Embodiments of the disclosed methods and system may be used to aid in the detection of cancer in mammogram images.

Voting in mammography processing

Handwritten word recognition - the approach proved by practice

This paper presents AddressScript - a system for handwritten postal address recognition for US mail. Key aspects of AddressScript technology, such as system control flow, cursive handwriting recognition, and postal database are described. Special attention is paid to the powerful character recognizer and the intensive usage of context, which becomes available during the recognition process. The algorithm of confidence level calculation is presented. Laboratory test results on a blind test set of 50,000 images of live hand-written mail pieces demonstrate a 64% finalization rate for error rates below USPS restrictions.

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Alexander Filatov

Papers

Holistic-analytical recognition of handwritten text

Check Amount Recognition Based on the Cross Validation of Courtesy and Legal Amount Fields

Voting in mammography processing

Handwritten word recognition - the approach proved by practice

The AddressScriptTM Recognition System for Handwritten Envelopes