Handwritten Character Recognition of South Indian Scripts: A Review

Handwritten character recognition is always a frontier area of research in the field of pattern recognition and image processing and there is a large demand for OCR on hand written documents. Even though, sufficient studies have performed in foreign scripts like Chinese, Japanese and Arabic characters, only a very few work can be traced for handwritten character recognition of Indian scripts especially for the South Indian scripts. This paper provides an overview of offline handwritten character recognition in South Indian Scripts, namely Malayalam, Tamil, Kannada and Telungu.Comment: Paper presented on the "National Conference on Indian Language Computing", Kochi, February 19-20, 2011. 6 pages, 5 figure

On-line Handwritten Character Recognition: An Implementation of Counterpropagation Neural Net

On-line handwritten scripts are usually dealt with pen tip traces from pen-down to pen-up positions. Time evaluation of the pen coordinates is also considered along with trajectory information. However, the data obtained needs a lot of preprocessing including filtering, smoothing, slant removing and size normalization before recognition process. Instead of doing such lengthy preprocessing, this paper presents a simple approach to extract the useful character information. This work evaluates the use of the counter- propagation neural network (CPN) and presents feature extraction mechanism in full detail to work with on-line handwriting recognition. The obtained recognition rates were 60% to 94% using the CPN for different sets of character samples. This paper also describes a performance study in which a recognition mechanism with multiple hresholds is evaluated for counter-propagation architecture. The results indicate that the application of multiple thresholds has significant effect on recognition mechanism. The method is applicable for off-line character recognition as well. The technique is tested for upper-case English alphabets for a number of different styles from different peoples

A Rule-based Methodology and Feature-based Methodology for Effect Relation Extraction in Chinese Unstructured Text

The Chinese language differs significantly from English, both in lexical representation and grammatical structure. These differences lead to problems in the Chinese NLP, such as word segmentation and flexible syntactic structure. Many conventional methods and approaches in Natural Language Processing (NLP) based on English text are shown to be ineffective when attending to these language specific problems in late-started Chinese NLP. Relation Extraction is an area under NLP, looking to identify semantic relationships between entities in the text. The term “Effect Relation” is introduced in this research to refer to a specific content type of relationship between two entities, where one entity has a certain “effect” on the other entity. In this research project, a case study on Chinese text from Traditional Chinese Medicine (TCM) journal publications is built, to closely examine the forms of Effect Relation in this text domain. This case study targets the effect of a prescription or herb, in treatment of a disease, symptom or body part. A rule-based methodology is introduced in this thesis. It utilises predetermined rules and templates, derived from the characteristics and pattern observed in the dataset. This methodology achieves the F-score of 0.85 in its Named Entity Recognition (NER) module; 0.79 in its Semantic Relationship Extraction (SRE) module; and the overall performance of 0.46. A second methodology taking a feature-based approach is also introduced in this thesis. It views the RE task as a classification problem and utilises mathematical classification model and features consisting of contextual information and rules. It achieves the F-scores of: 0.73 (NER), 0.88 (SRE) and overall performance of 0.41. The role of functional words in the contemporary Chinese language and in relation to the ERs in this research is explored. Functional words have been found to be effective in detecting the complex structure ER entities as rules in the rule-based methodology

Multimedia information technology and the annotation of video

The state of the art in multimedia information technology has not progressed to the point where a single solution is available to meet all reasonable needs of documentalists and users of video archives. In general, we do not have an optimistic view of the usability of new technology in this domain, but digitization and digital power can be expected to cause a small revolution in the area of video archiving. The volume of data leads to two views of the future: on the pessimistic side, overload of data will cause lack of annotation capacity, and on the optimistic side, there will be enough data from which to learn selected concepts that can be deployed to support automatic annotation. At the threshold of this interesting era, we make an attempt to describe the state of the art in technology. We sample the progress in text, sound, and image processing, as well as in machine learning