ARTICLE vincia03b-art/IDIAP Offline Recognition of Unconstrained Handwritten Texts Using HMMs and Statistical Language Models Vinciarelli, Alessandro Bengio, Samy Bunke, Horst EXTERNAL https://publications.idiap.ch/attachments/reports/2003/rr03-22.pdf PUBLIC https://publications.idiap.ch/index.php/publications/showcite/vin03b Related documents IEEE Transactions on Pattern Analysis and Machine Intelligence 26 6 709-720 2004 IDIAP-RR 03-22 This paper presents a system for the offline recognition of large vocabulary unconstrained handwritten texts. The only assumption made about the data is that it is written in English. This allows the application of Statistical Language Models in order to improve the performance of our system. Several experiments have been performed using both single and multiple writer data. Lexica of variable size (from 10,000 to 50,000 words) have been used. The use of language models is shown to improve the accuracy of the system (when the lexicon contains 50,000 words, error rate is reduced by 50% for single writer data and by 25% for multiple writer data). Our approach is described in detail and compared with other methods presented in the literature to deal with the same problem. An experimental setup to correctly deal with unconstrained text recognition is proposed. REPORT vin03b/IDIAP Offline Recognition of Unconstrained Handwritten Texts Using HMMs and Statistical Language Models Vinciarelli, Alessandro Bengio, Samy Bunke, Horst EXTERNAL https://publications.idiap.ch/attachments/reports/2003/rr03-22.pdf PUBLIC Idiap-RR-22-2003 2003 IDIAP Accepted for publication by IEEE Transactions on Pattern Analysis and Machine Intelligence This paper presents a system for the offline recognition of large vocabulary unconstrained handwritten texts. The only assumption made about the data is that it is written in English. This allows the application of Statistical Language Models in order to improve the performance of our system. Several experiments have been performed using both single and multiple writer data. Lexica of variable size (from 10,000 to 50,000 words) have been used. The use of language models is shown to improve the accuracy of the system (when the lexicon contains 50,000 words, error rate is reduced by 50% for single writer data and by 25% for multiple writer data). Our approach is described in detail and compared with other methods presented in the literature to deal with the same problem. An experimental setup to correctly deal with unconstrained text recognition is proposed.