Off-Line Cursive Script Word Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Artificial intelligence (3rd ed.)
Artificial intelligence (3rd ed.)
Interpreting handwritten text in a constrained domain
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Off-line cursive script recognition: current advances, comparisons and remaining problems
Artificial Intelligence Review
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A recognition system for general isolated offline handwritten words using an approximate segment-string matching algorithm is described. The fundamental paradigm employed is a character-based segment-then-recognize/match strategy. Additional user supplied contextual information in the form of a lexicon guides a graph search to estimate the most likely word image identity. This system is designed to operate robustly in the presence of document noise, poor handwriting, and lexicon errors, so this basic strategy is significantly extended and enhanced. A preprocessing step is initially applied to the image to remove noise artifacts and normalize the handwriting. An oversegmentation approach is taken to improve the likelihood of capturing the individual characters embedded in the word. The goal is to produce a segmentation point set that contains one subset which is the correct segmentation of the word image. This is accomplished by a segmentation module, employing several independent detection rules based on certain key features, which finds the most likely segmentation points of the word. Next, a sliding window algorithm, using a character recognition algorithm with a very good noncharacter rejection response, is used to find the most likely character boundaries and identities. A directed graph is then constructed that contains many possible interpretations of the word image, many implausible. Contextual information is used at this point and the lexicon is matched to the graph in a breath-first manner, under an appropriate metric. The matching algorithm employs a BEAM search algorithm with several heuristics to compensate for the most likely errors contained in the interpretation graph, including missing segments from segmentation failures, misrecognition of the segments, and lexicon errors. The most likely graph path and associated confidence is computed for each lexicon word to produce a final lexicon ranking. These confidences are very reliable and can be later thresholded to decrease total recognition error. Experiments highlighting the characteristics of this algorithm are given.