Bridging the Gap in Resource for Offline English Handwritten Text Recognition

The practical applications of Handwritten Text Recognition (HTR) have flourished with many successful commercial APIs, solutions, and diverse use cases. Despite the availability of numerous industrial solutions, academic research in HTR, particularly for English, has been hindered by the scarcity of publicly accessible data. To bridge this gap, this paper introduces IIIT-HW-English-Word, a large and diverse collection of offline handwritten English documents. This dataset comprises unconstrained camera-captured images featuring 20,800 handwritten documents crafted by 1,215 writers. Within this dataset, covering 757,830 words, we identify 174,701 unique words encompassing a variety of content types, such as alphabetic, numeric, and stop-words. We also establish a baseline for the proposed dataset, facilitating evaluation and benchmarking, explicitly focusing on word recognition tasks. Our findings suggest that our dataset can effectively serve as a training source to enhance performance on respective datasets. The code, dataset, and benchmark results are available at  https://cvit.iiit.ac.in/usodi/bgroehtr.php .