Gregory R. Ball

Offline Chinese handwriting recognition: an assessment of current technology

Offline Chinese handwriting recognition (OCHR) is a typically difficult pattern recognition problem. Many authors have presented various approaches to recognizing its different aspects. We present a survey and an assessment of relevant papers appearing in recent publications of relevant conferences and journals, including those appearing in ICDAR, SDIUT, IWFHR, ICPR, PAMI, PR, PRL, SPIEDRR, and IJDAR. The methods are assessed in the sense that we document their technical approaches, strengths, and weaknesses, as well as the data sets on which they were reportedly tested and on which results were generated. We also identify a list of technology gaps with respect to Chinese handwriting recognition and identify technical approaches that show promise in these areas as well as identify the leading researchers for the applicable topics, discussing difficulties associated with any given approach.

Versatile search of scanned Arabic handwriting

Searching handwritten documents is a relatively unexplored frontier for documents in any language. Traditional approaches use either image-based or text-based techniques. This paper describes a framework for versatile search where the query can be either text or image, and the retrieval method fuses text and image retrieval methods. A UNICODE and an image query are maintained throughout the search, with the results being combined by a neural network. Preliminary results show positive results that can be further improved by refining the component pieces of the framework (text transcription and image search).

An Assessment of Arabic Handwriting Recognition Technology

Automated methods for the recognition of Arabic script are at an early stage compared to their counterparts for the recognition of Latin and Chinese scripts. An assessment of the technology for Arabic handwriting recognition is provided based on the published literature. An introduction to the Arabic script is given followed by a description of algorithms for the processes involved: segmentation, feature extraction, classification, and search. Existing corpora for Arabic are described together with a design for corpus collection. The paper is concluded by identifying technology gaps and providing a bibliography of the recent literature on Arabic recognition.

Language Independent Word Spotting in Scanned Documents

Large quantities of scanned handwritten and printed documents are rapidly being made available for use by information storage and retrieval systems, such as for use by libraries. We present the design and performance of a language independent system for spotting handwritten/printed words in scanned document images. The technique is evaluated with three scripts: Devanagari (Sanskrit/Hindi), Arabic (Arabic/Urdu) and Latin (English). Three main components of the system are a word segmenter, a shape based matcher for words, and a search interface. The user gives a query which can be (i) A word image (to spot similar words from a collection of documents written in that script) or (ii) text (to look for the equivalent word images in the script). The candidate words that are searched in the documents are retrieved and ranked, where the ranking criterion is a similarity score between the query and the candidate words based on global word shape features. For handwritten English, a precision of 60% was obtained at a recall of 50%. An alternate approach comprising of prototype selection and word matching, that yields a better performance for handwritten documents is also discussed. For printed Sanskrit documents, a precision as high as 90% was obtained at a recall of 50%.

Comparison of statistical models for writer verification

A novel statistical model for determining whether a pair of documents, a known and a questioned, were written by the same individual is proposed. The goal of this formulation is to learn the specific uniqueness of style in a particular authors writing, given the known document. Since there are often insufficient samples to extrapolate a generalized model of an writers handwriting based solely on the document, we instead generalize over the differences between the author and a large population of known different writers. This is in contrast to an earlier model proposed whereby probability distributions were a priori without learning. We show the performance of the model along with a comparison in performance to the non-learning, older model, which shows significant improvement.

Writer Verification of Historical Documents among Cohort Writers

Over the last century forensic document science has developed progressively more sophisticated pattern recognition methodologies for ascertaining the authorship of disputed documents. We present a writer verification method and an evaluation of its performance on historical documents with known and unknown writers. The questioned document is compared against handwriting samples of Herman Melville, a 19th century American author who has been hypothesized to be the writer as well as against samples crafted by several writers from the same time period. The comparison led to a high confidence result to the questioned documents writer ship, as well as gives evidence for the validity of the writer verification method in the context of historical documents. Such methodology can be applied to many such questioned historical documents, both in literary and legal fields.

Writer adaptation in off-line Arabic handwriting recognition

Writer adaptation or specialization is the adjustment of handwriting recognition algorithms to a specific writers style of handwriting. Such adjustment yields significantly improved recognition rates over counterpart general recognition algorithms. We present the first unconstrained off-line handwriting adaptation algorithm for Arabic presented in the literature. We discuss an iterative bootstrapping model which adapts a writer-independent model to a writer-dependent model using a small number of words achieving a large recognition rate increase in the process. Furthermore, we describe a confidence weighting method which generates better results by weighting words based on their length. We also discuss script features unique to Arabic, and how we incorporate them into our adaptation process. Even though Arabic has many more character classes than languages such as English, significant improvement was observed. The testing set consisting of about 100 pages of handwritten text had an initial average overall recognition rate of 67%. After the basic adaptation was finished, the overall recognition rate was 73.3%. As the improvement was most marked for the longer words, and the set of confidently recognized longer words contained many fewer false results, a second method was presented using them alone, resulting in a recognition rate of about 75%. Initially, these words had a 69.5% recognition rate, improving to about a 92% recognition rate after adaptation. A novel hybrid method is presented with a rate of about 77.2%.

Writer Verification of Arabic Handwriting

Expanding on an earlier study to objectively validate the hypothesis that handwriting is individualistic, we extend the study to include handwriting in the Arabic script. Handwriting samples from twelve native speakers of Arabic were obtained. Analyzing differences in handwriting was done by using computer algorithms for extracting features from scanned images of handwriting. Attributes characteristic of the handwriting were obtained, e.g., line separation, slant, character shapes, etc. These attributes, which are a subset of attributes used by forensic document examiners (FDEs), were used to quantitatively establish individuality by using machine learning approaches. Using global attributes of handwriting, the ability to determine the writer with a high degree of confidence was established. The work is a step towards providing scientific support for admitting handwriting evidence in court.

Comparison of historical documents for writership

Over the last century forensic document science has developed progressively more sophisticated pattern recognition methodologies for ascertaining the authorship of disputed documents. These include advances not only in computer assisted stylometrics, but forensic handwriting analysis. We present a writer verification method and an evaluation of an actual historical document written by an unknown writer. The questioned document is compared against two known handwriting samples of Herman Melville, a 19th century American author who has been hypothesized to be the writer of this document. The comparison led to a high confidence result that the questioned document was written by the same writer as the known documents. Such methodology can be applied to many such questioned documents in historical writing, both in literary and legal fields.

Statistical Characterization of Handwriting Characteristics Using Automated Tools

We provide a statistical basis for reporting the results of handwriting examination by questioned document (QD) examiners. As a facet of Questioned Document (QD) examination, the analysis and reporting of handwriting examination suffers from the lack of statistical data concerning the frequency of occurrence of combinations of particular handwriting characteristics. QD examiners tend to assign probative values to specific handwriting characteristics and their combinations based entirely on the examiners experience and power of recall. The research uses data bases of handwriting samples that are representative of the US population. Feature lists of characteristics provided by QD examiners, are used to determine as to what frequencies need to be evaluated. Algorithms are used to automatically extract those characteristics, e.g., a software tool for extracting most of the characteristics from the most common letter pair th, is functional. For each letter combination the marginal and conditional frequencies of their characteristics are evaluated. Based on statistical dependencies of the characteristics the probability of any given letter formation is computed. The resulting algorithms are incorporated into a system for writer verification known as CEDAR-FOX.