Abstract

This study evaluates the performance of transfer learning using the VGG16 model for handwritten text recognition, integrating Word Beam Search decoding and language modeling techniques. The VGG16 model, pre-trained on large-scale datasets, serves as a feature extractor for handwritten text images, capturing intricate patterns and structures inherent in handwriting. To convert these visual features into textual information, the system employs a Recurrent Neural Network (RNN) trained with the Connectionist Temporal Classification (CTC) loss function, producing a matrix of character probabilities for each time-step. The Word Beam Search algorithm is utilized for decoding these probabilities into coherent text, effectively constructing recognized text by referencing a predefined dictionary and addressing challenges such as arbitrary character strings and varying handwriting styles. The integration of language models incorporates context which further sharpens the output and improves precision and trustworthiness of recognition systems. Experimental results demonstrate that this combined approach significantly improves recognition performance, highlighting the efficacy of transfer learning and advanced decoding strategies in handwritten text recognition. This involves analyzing its effectiveness across various datasets. Transfer learning leverages pre-trained models, like VGG16, to address challenges such as limited labeled data and extensive training times.