Analysis of Preprocessing Techniques for Latin Handwriting Recognition

Abstract

In this work we analyze the contribution of preprocessing steps for Latin handwriting recognition. A preprocessing pipeline based on geometric heuristics and image statistics is used. This pipeline is applied to French and English handwriting recognition in an HMM based framework. Results show that preprocessing improves recognition performance for the two tasks. The Maximum Likelihood (ML)-trained HMM system reaches a competitive WER of 16.7% and outperforms many sophisticated systems for the French handwriting recognition task. The results for English handwriting are comparable to other ML-trained HMM recognizers. Using MLP preprocessing a WER of 35.3% is achieved.