Preprocessing Techniques in Character Recognition

The advancements in pattern recognition has accelerated recently due to the many emerging applications which are not only challenging, but also computationally more demanding, such evident in Optical Character Recognition (OCR), Document Classification, Computer Vision, Data Mining, Shape Recognition, and Biometric Authentication, for instance. The area of OCR is becoming an integral part of document scanners, and is used in many applications such as postal processing, script recognition, banking, security (i.e. passport authentication) and language identification. The research in this area has been ongoing for over half a century and the outcomes have been astounding with successful recognition rates for printed characters exceeding 99%, with significant improvements in performance for handwritten cursive character recognition where recognition rates have exceeded the 90% mark. Nowadays, many organizations are depending on OCR systems to eliminate the human interactions for better performance and efficiency. The field of pattern recognition is a multidisciplinary field which forms the foundation of other fields, as for instance, Image Processing, Machine Vision, and Artificial Intelligence. Therefore, OCR cannot be applied without the help of Image Processing and/or Artificial Intelligence. Any OCR system goes through numerous phases including: data acquisition, preprocessing, feature extraction, classification and post-processing where the most crucial aspect is the preprocessing which is necessary to modify the data either to correct deficiencies in the data acquisition process due to limitations of the capturing device sensor, or to prepare the data for subsequent activities later in the description or classification stage. Data preprocessing describes any type of processing performed on raw data to prepare it for another processing procedure. Hence, preprocessing is the preliminary step which transforms the data into a format that will be more easily and effectively processed. Therefore, the main task in preprocessing the captured data is to decrease the variation that causes a reduction in the recognition rate and increases the complexities, as for example, preprocessing of the input raw stroke of characters is crucial for the success of efficient character recognition systems. Thus, preprocessing is an essential stage prior to feature extraction since it controls the suitability of the results for the successive stages. The stages in a pattern recognition system are in a pipeline fashion meaning that each stage depends on the success of the previous stage in order to produce optimal/valid results. However, it is 1