Background:
String matching is crucial when verifying text extracted from images or other sources. However, OCR tools often introduce errors, making exact string matching unreliable. This raises the need for an efficient algorithm to compare extracted strings against a dataset, even in the presence of errors.
Approach:
While using Spark for this task may not be ideal, we present an approach that combines multiple machine learning transformers:
Implementation:
<code class="scala">import org.apache.spark.ml.feature.{RegexTokenizer, NGram, HashingTF, MinHashLSH, MinHashLSHModel} val tokenizer = new RegexTokenizer() val ngram = new NGram().setN(3) val vectorizer = new HashingTF() val lsh = new MinHashLSH() val pipeline = new Pipeline() val model = pipeline.fit(db) val dbHashed = model.transform(db) val queryHashed = model.transform(query) model.stages.last.asInstanceOf[MinHashLSHModel] .approxSimilarityJoin(dbHashed, queryHashed, 0.75).show</code>
This approach leverages LSH to efficiently identify similar strings, even with errors. The threshold of 0.75 can be adjusted depending on the desired level of similarity.
Pyspark Implementation:
<code class="python">from pyspark.ml import Pipeline from pyspark.ml.feature import RegexTokenizer, NGram, HashingTF, MinHashLSH model = Pipeline(stages=[ RegexTokenizer(pattern="", inputCol="text", outputCol="tokens", minTokenLength=1), NGram(n=3, inputCol="tokens", outputCol="ngrams"), HashingTF(inputCol="ngrams", outputCol="vectors"), MinHashLSH(inputCol="vectors", outputCol="lsh") ]).fit(db) db_hashed = model.transform(db) query_hashed = model.transform(query) model.stages[-1].approxSimilarityJoin(db_hashed, query_hashed, 0.75).show()</code>
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