用深度学习提升手写数字识别，助力乡村女性语音平台大规模触达
Use of Metric Learning for the Recognition of Handwritten Digits, and its Application to Increase the Outreach of Voice-based Communication Platforms
- 基于自建手写数字数据集，开发适用于真实场景的OCR模型
- 在印度北部项目中实现400万次语音呼叫，通过纸面表单自动识别电话号码
- 开源数据、模型与代码，支持发展项目中的低成本数据采集
发展项目的需求调研、实施监测与效果评估常依赖实地数据收集。然而，由于基层人员无法负担智能手机或平板设备，或缺乏使用培训，数字化设备采集数据往往不可行。在此背景下，纸质表单被证明更具适用性，可通过OCR（光学字符识别）和OMR（光学标记识别）技术实现自动化数字化。本文贡献了一个大规模手写数字数据集，以及基于该数据构建的深度学习模型与方法，具备在真实环境中的有效性。我们将其应用于北印度母婴健康与营养意识项目，该项目通过IVR（交互式语音应答）系统向农村妇女自组织小组成员提供信息。采用纸质表单大规模收集小组成员手机号码，利用我们开发的OCR工具完成数字化处理，成功推送近400万次电话呼叫。相关数据、模型与代码已开源。
原文摘要 · Abstract (English)
Initiation, monitoring, and evaluation of development programmes can involve field-based data collection about project activities. This data collection through digital devices may not always be feasible though, for reasons such as unaffordability of smartphones and tablets by field-based cadre, or shortfalls in their training and capacity building. Paper-based data collection has been argued to be more appropriate in several contexts, with automated digitization of the paper forms through OCR (Optical Character Recognition) and OMR (Optical Mark Recognition) techniques. We contribute with providing a large dataset of handwritten digits, and deep learning based models and methods built using this data, that are effective in real-world environments. We demonstrate the deployment of these tools in the context of a maternal and child health and nutrition awareness project, which uses IVR (Interactive Voice Response) systems to provide awareness information to rural women SHG (Self Help Group) members in north India. Paper forms were used to collect phone numbers of the SHG members at scale, which were digitized using the OCR tools developed by us, and used to push almost 4 million phone calls. The data, model, and code have been released in the open-source domain.
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