In this paper, an algorithm is proposed for license plate recognition (LPR) in video traffic surveillance applications. In an LPR system, the primary steps are license plate detection and character segmentation. However, in practice, false alarms often occur due to images of vehicle parts that are similar in appearance to a license plate or detection rate degradation due to local illumination changes. To alleviate these difficulties, the proposed license plate segmentation employs an adaptive binarization using a superpixel-based local contrast measurement. From the binarization, we apply a set of rules to a sequence of characters in a sub-image region to determine whether it is part of a license plate. This process is effective in reducing false alarms and improving detection rates. Our experimental results demonstrate a significant improvement over conventional methods.

This paper proposes a method for cursive handwritten word recognition. Cursive word recognition generally consists of segmentation of a cursive word, character recognition and word recognition. Traditional approaches detect one candidate of segmentation point between characters, and cut the touching characters at the point [1]. But, it is difficult to detect a correct segmentation point between characters in cursive word, because form of touching characters varies greatly by cases. In this research, we determine multiple candidates as segmentation points between characters. Character recognition and word recognition decide which candidate is the most plausible touching point. As a result of the experiment, at the character recognition stage, recognition rate was 75.7%, while cumulative recognition rate within best three candidates was 93.7%. In word recognition, recognition rate was 79.8%, while cumulative recognition rate within best five candidates was 91.7% when lexicon size is 50. The processing speed is about 30 sec/word on SPARC station 5.

The global interpolation method we proposed can extract a handwritten alpha-numeric character pattern even if it overlaps a border. Our method interpolates blank segments in a character after borders are removed by evaluating segment pattern continuity and connectedness globally to produce characters with smooth edges. The main feature of this method is to evaluate global component label connectivity as pattern connectedness. However, it is impossible for the method to interpolate missing superpositioning loop segments, because they lack segment pattern continuity and they have already had global component label connectivity. To solve this problem, we improved the method by adding loop interpolation as a global evaluation. The evaluation of character segment continuity is also improved to achieve higher quality character patterns. There is no database of overlapping characters, so we also propose an evaluation method which generates various kinds of overlapping numerals from an ETL database. Experimental results using these generated patterns showed that the improved global interpolation method is very effective for numbers that overlap a border.

The global interpolation method we propose evaluates segment pattern continuity and connectedness to produce characters with smooth edges while interpreting blank or missing segments based on global label connectivities, e.g, in extracting a handwritten character overlapping a border, correctly. Conventional character segmentation involving overlapping a border concentrates on removing the thin border based on known format information rather than extracting the character. This generates discontinuous segments which produce distortion due to thinning and errors in direction codes, and is the problem to recognize the extracted character. In our method, characters contacting a border are extracted after the border itself is labeled and removed automatically by devising how to extract wavy and oblique borders involved in fax communication. The absence of character segments is then interpolated based on segment continuity. Interpolated segments are relabeled and checked for matching against the original labeled pattern. If a match cannot be made, segments are reinterpolated until they can be identified. Experimental results show that global interpolation interprets the absence of character segments correctly and generates with smooth edges.