# TMIXT: A process flow for Transcribing MIXed handwritten and machine-printed Text

## TL;DR

This paper introduces TMIXT, a process flow that enables effective transcription of scanned documents containing both handwritten and machine-printed text without prior classification, achieving nearly 80% accuracy.

## Contribution

The work presents a novel, generic process flow for mixed text recognition that eliminates the need for pre-classification of text types in scanned documents.

## Key findings

- Achieved nearly 80% transcription accuracy on mixed text documents.
- Developed a new variant of the IAM handwriting database for evaluation.
- Demonstrated effectiveness of open-source tools in the process flow.

## Abstract

Handling large corpuses of documents is of significant importance in many fields, no more so than in the areas of crime investigation and defence, where an organisation may be presented with a large volume of scanned documents which need to be processed in a finite time. However, this problem is exacerbated both by the volume, in terms of scanned documents and the complexity of the pages, which need to be processed. Often containing many different elements, which each need to be processed and understood. Text recognition, which is a primary task of this process, is usually dependent upon the type of text, being either handwritten or machine-printed. Accordingly, the recognition involves prior classification of the text category, before deciding on the recognition method to be applied. This poses a more challenging task if a document contains both handwritten and machine-printed text. In this work, we present a generic process flow for text recognition in scanned documents containing mixed handwritten and machine-printed text without the need to classify text in advance. We realize the proposed process flow using several open-source image processing and text recognition packages1. The evaluation is performed using a specially developed variant, presented in this work, of the IAM handwriting database, where we achieve an average transcription accuracy of nearly 80% for pages containing both printed and handwritten text.